Photovoltaic power generation has become a central pillar of the global energy transition. According to recent industry forecasts, global new photovoltaic installations are expected to reach 596 GW in 2025, with China contributing more than 44% of this capacity. However, solar panels are continuously exposed to harsh environmental conditions, which makes them susceptible to various small-scale faults such as microcracks, hot spots, finger electrode interruptions, and grid line anomalies. These small-target faults occupy only a tiny fraction of the image pixels, making their features easily overwhelmed by background noise. Moreover, the same fault type can exhibit significant scale variations, further complicating accurate detection and classification. To address these challenges, I have selected YOLOv9 as the foundational algorithm and introduced targeted improvements to enhance small-target detection performance.
In my research, I propose two optimized algorithms based on YOLOv9: YOLOv9+SPDConv+EMA (which I denote as YOLOv9-SPDEMA) and YOLOv9+DySample+AIFI (denoted as YOLOv9-AIFIDS). These two approaches focus on different stages of the detection pipeline: the first path enhances detail feature extraction and channel attention, while the second optimizes dynamic upsampling and feature interaction. Through comprehensive experiments on a photovoltaic panel infrared image dataset, I validate the effectiveness of both methods for dense small-target detection tasks.
1. Research Background and Challenges
The reliability of solar panels directly determines the efficiency and safety of photovoltaic power stations. A study from the US National Renewable Energy Laboratory indicates that the median failure rate of solar panels installed between 2000 and 2015 is 5 per 10,000 modules, meaning that one out of every 2,000 panels must be replaced due to failure. Given China’s cumulative installed photovoltaic capacity exceeding 886 GW in 2024, the number of faulty panels could reach hundreds of thousands. Without timely detection and maintenance, these faults not only reduce power generation efficiency but also pose significant safety risks, including fire hazards caused by hot spots.
Small-target faults in solar panels include microcracks, hot spots, finger interruptions, and grid line defects. Although each individual fault may appear minor, the cumulative effect can substantially reduce the energy yield. For instance, a single hot spot can cause a local temperature rise, leading to a decrease in module efficiency by over 30% in severe cases. Early-stage microcracks are particularly difficult to detect, with a missed detection rate as high as 40% in conventional inspection routines.
The primary difficulties in small-target fault detection for solar panels are:
- Low pixel occupancy: Small-target faults often occupy less than 5% of the total image area. For example, a microcrack may be only a few millimeters wide, representing only a handful of pixels even in high-resolution infrared images. Traditional object detection algorithms rely heavily on shape and texture features, which become unreliable when the target contains too few pixels.
- Multi-scale distribution: The same fault type can vary dramatically in size. For instance, hot spots may start as a few millimeters in diameter and expand to several centimeters as the fault progresses. Standard feature pyramid networks often struggle to simultaneously preserve fine details for small instances and sufficient semantic context for larger ones.
- Environmental interference: Changes in illumination, reflections, shadows, and non-uniform temperatures of the solar panel surface can obscure small fault features or create false positives. For example, shadows cast by nearby objects may resemble hot spots in infrared images, leading to misclassification.
In this study, I specifically focus on three types of small-target faults: Crack, Finger, and Thick_line. These fault categories are characterized by small pixel occupancy and low contrast in infrared images, making them ideal benchmarks for evaluating the performance of small-target detection algorithms.
2. Fundamentals of Deep Learning for Object Detection
Convolutional neural networks (CNNs) form the backbone of most modern object detectors. A typical CNN consists of convolutional layers, activation functions, pooling layers, fully connected layers, and an output layer. The convolutional layer performs feature extraction by applying learnable kernels to the input image. The operation can be expressed as:
$$ Z^{(l)}_{i,j,k} = \sum_{m=0}^{M-1} \sum_{n=0}^{N-1} W^{(l)}_{m,n,k} \cdot X^{(l-1)}_{i+m, j+n} + b^{(l)}_k $$
where \( Z^{(l)}_{i,j,k} \) is the output feature value at spatial position \((i,j)\) for the \(k\)-th filter in layer \(l\), \( W \) is the kernel weight, \( X \) is the input, and \( b \) is the bias term.
Pooling layers reduce the spatial dimensions of feature maps, thereby lowering computational complexity while retaining important features. The two most common pooling operations are max pooling and average pooling. The output size after pooling is given by:
$$ H_{out} = \frac{H_{in} + 2P – K}{S} + 1 $$
where \( H_{in} \) and \( H_{out} \) are the input and output heights, \( P \) is padding, \( K \) is the kernel size, and \( S \) is the stride.
Fully connected layers integrate high-level semantic features and map them to class probabilities or regression outputs. In classification tasks, the softmax function is often used in the output layer:
$$ \text{Softmax}(x_i) = \frac{e^{x_i}}{\sum_{j=1}^{C} e^{x_j}} $$
The evaluation of object detection models relies on metrics such as Precision, Recall, F1-score, Intersection over Union (IoU), Average Precision (AP), and mean Average Precision (mAP). These metrics are defined as follows:
$$ \text{Precision} = \frac{TP}{TP + FP} $$
$$ \text{Recall} = \frac{TP}{TP + FN} $$
$$ F1 = \frac{2 \times \text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}} $$
$$ IoU = \frac{\text{Area of Intersection}}{\text{Area of Union}} $$
The mAP is calculated by averaging the AP over all classes. Typically, mAP@0.5 and mAP@0.5:0.95 are reported, where 0.5:0.95 indicates the mean AP across IoU thresholds from 0.5 to 0.95 with a step size of 0.05.
3. YOLOv9 Architecture Overview
YOLOv9 introduces two key innovations: Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN). PGI addresses the information bottleneck problem in deep neural networks by providing auxiliary supervision signals through a reversible branch. This additional gradient information helps the model converge more reliably and improves detection accuracy. GELAN combines the design concepts of CSPNet and ELAN, using a computation block (such as a bottleneck or residual block) to achieve efficient feature aggregation with optimized gradient paths.
The network structure of YOLOv9 includes:
- Backbone: GELAN-based architecture with repeated RepNCSPELAN4 modules and downsampling blocks.
- Neck: Feature pyramid network (FPN) and path aggregation network (PAN) for multi-scale feature fusion.
- Head: Decoupled detection heads for classification and regression.
The RepConvN module is a re-parameterized convolutional block. In the training stage, it uses multiple branches (e.g., 3×3 and 1×1 convolutions), each followed by batch normalization. During inference, these branches are fused into a single 3×3 convolution, reducing computational overhead while maintaining accuracy. The fusion process can be expressed as:
$$ W_{fused} = W_{3\times3} + W_{1\times1} \quad \text{and} \quad b_{fused} = b_{3\times3} + b_{1\times1} $$
The RepNCSPELAN4 module combines the RepConvN block with the CSP (Cross Stage Partial) structure and the ELAN (Efficient Layer Aggregation Network) module to enhance multi-scale feature extraction.
Although YOLOv9 performs well on general object detection, it still faces challenges when applied to small targets in low-resolution infrared images. The main issue arises from strided convolutions and pooling operations, which progressively reduce spatial resolution and cause loss of fine-grained information. To overcome this limitation, I propose two improved frameworks, each addressing the problem from a different perspective.
4. YOLOv9-SPDEMA: Detail-Enhanced Feature Extraction
The first optimization path focuses on preserving fine-grained features during downsampling and enhancing cross-scale attention. This is achieved by integrating two modules: SPDConv (Space-to-Depth Convolution) and EMA (Efficient Multi-scale Attention).
4.1 SPDConv
SPDConv is a convolutional building block designed to handle low-resolution images and small objects. It consists of a Space-to-Depth (SPD) layer followed by a non-strided convolution. The SPD layer re-arranges spatial information into the depth dimension, thereby avoiding information loss caused by strided convolutions or pooling.
For an input feature map \( X \) of size \( H \times W \times C_1 \) and a downscaling factor \( s \), the SPD layer divides \( X \) into \( s \times s \) sub-blocks and concatenates them along the channel dimension:
$$ \text{SPD}(X) = \text{Concat}(X_{0,0}, X_{0,1}, \ldots, X_{s-1,s-1}) $$
where \( X_{i,j} \) is a sub-block of size \( \frac{H}{s} \times \frac{W}{s} \times C_1 \). The output has the shape \( \frac{H}{s} \times \frac{W}{s} \times (s^2 C_1) \). When \( s = 2 \), the operation is:
$$ X_{spd} = \text{Concat}(X[0::2, 0::2, :], X[0::2, 1::2, :], X[1::2, 0::2, :], X[1::2, 1::2, :]) $$
After the SPD layer, a non-strided convolution (stride = 1) is applied to the transformed feature map. This convolution preserves the spatial resolution while refining the feature representation. The output is given by:
$$ Y = \text{Conv}(X_{spd}, W) + b $$
By replacing all strided convolutions and pooling layers with SPDConv, the model retains much higher fidelity for small structures such as microcracks and narrow grid lines.
4.2 EMA Attention Module
EMA (Efficient Multi-scale Attention) is a lightweight attention mechanism that avoids channel dimensionality reduction. It reshapes some channels into batch dimensions and divides the remaining channels into multiple sub-feature groups. This design ensures that spatial semantic information is evenly distributed across groups, preserving fine details. The structure of EMA consists of two parallel branches: one captures global information through average pooling and softmax operations, while the other processes local features through convolution, batch normalization, and activation functions. The outputs of the two branches are fused via cross-dimension interaction to generate attention weights for pixel-level recalibration.
The attention weight calculation can be summarized as:
$$ \text{Att} = \text{Softmax}\left( \frac{QK^T}{\sqrt{d}} \right) \cdot V $$
where \( Q \), \( K \), and \( V \) are query, key, and value matrices derived from the input feature groups. The use of exponential moving average (EMA) smoothing on the attention scores stabilizes the training process and suppresses noise:
$$ \text{EMA\_scores}_{t} = \alpha \cdot \text{EMA\_scores}_{t-1} + (1-\alpha) \cdot \text{softmax}\left( \frac{QK^T}{\sqrt{d}} \right) $$
This smoothed attention helps the model focus on fault regions while reducing background interference.
4.3 Integration into YOLOv9
In the YOLOv9-SPDEMA framework, I replace the traditional strided downsampling layers in the YOLOv9 backbone with SPDConv modules. Specifically, within each RepNCSPELAN4 block, the standard convolution paths are modified to include SPDConv before the non-strided convolution. This allows the backbone to preserve high-resolution detail throughout the feature extraction process. In addition, I introduce a P2 detection layer that leverages the high-resolution features produced by the SPDConv, directly improving small-target localization.
In the Neck network, the BiFPN structure is enhanced by inserting the EMA module after each cross-scale feature fusion step. This enables the model to dynamically re-weigh features from different scales, emphasizing channels that are critical for detecting small faults on solar panels. The overall architecture of YOLOv9-SPDEMA is shown conceptually with the following stages:
- Input: Infrared image of size 2048×2048.
- Backbone: GELAN with SPDConv replacements, outputting feature maps S3 (52×52), S4 (26×26), and S5 (13×13).
- Neck: FPN/PAN with EMA attention for multi-scale feature fusion.
- Detection Head: Decoupled head with an extra P2 branch for high-resolution small-target detection.
5. YOLOv9-AIFIDS: Efficient Upsampling and Feature Interaction
While the first method focuses on preserving detail during downsampling, the second method improves the quality of upsampled features and enhances intra-scale feature interaction. The two key modules are AIFI (Attention-based Intra-Scale Feature Interaction) and DySample (Dynamic Sampling).
5.1 AIFI
AIFI is a Transformer-style module that applies multi-head self-attention within a single scale of feature maps. It is designed to capture long-range dependencies and enhance the semantic representation of high-level features. The attention mechanism is defined as:
$$ \text{Attention}(Q, K, V) = \text{softmax}\left( \frac{QK^T}{\sqrt{d_k}} \right) V $$
where \( Q = XW_Q \), \( K = XW_K \), \( V = XW_V \), and \( X \) is the input feature sequence. AIFI also integrates learnable position embeddings to preserve spatial information, which is essential for accurate localization of solar panel faults. In my implementation, I replace the SPPF module in YOLOv9 with the AIFI module. This substitution is particularly beneficial for small-target detection because AIFI can model contextual relationships between a small fault and its surroundings, thereby improving the discriminative power of the features. Moreover, AIFI operates only on the high-level S5 feature map, which reduces computational overhead while still providing significant performance gains.
5.2 DySample
DySample is a lightweight dynamic upsampler that generates content-adaptive sampling offsets. Unlike traditional interpolation methods (such as bilinear or nearest neighbor), DySample learns to shift the sampling grid based on the input feature content, thereby preserving sharp edges and fine details. The upsampling process can be summarized as follows:
- Generate offsets \( O \) from the input feature map \( X \) using a convolution:
$$ O = \text{Conv}(X) $$
- Combine offsets with the initial sampling grid \( G \) to obtain the final sampling set \( S \):
$$ S = G + O $$
- Apply grid sampling with bilinear interpolation to produce the upsampled feature map \( X’ \):
$$ X’ = \text{GridSample}(X, S) $$
In my work, I enhance DySample by introducing a dynamic scaling factor that weights the original feature map before fusion. This modification preserves more of the original fine-grained information, which is critical for small solar panel faults. The improved DySample module reduces the number of parameters by approximately 30% compared to standard dynamic upsampling methods, while achieving better feature restoration.
5.3 Integration into YOLOv9
In the YOLOv9-AIFIDS framework, DySample is used in the feature pyramid network (FPN) to replace all traditional upsampling layers. This ensures that during feature fusion, the upsampled feature maps retain high-frequency details that are essential for detecting tiny cracks and electrodes. The AIFI module is inserted at the point where the highest-level features (S5) enter the neck network, serving as a powerful feature processor before multi-scale fusion begins.
The overall flow of YOLOv9-AIFIDS is:
- Backbone: Same as YOLOv9, generating S3, S4, S5 features.
- AIFI: Applied to S5 to enhance semantic context and long-range dependencies.
- Neck with DySample: The FPN path uses DySample for upsampling (S5→S4, S4→S3). Each upsampled feature is fused with the corresponding backbone feature via element-wise addition followed by a 3×3 convolution.
- Detection Head: Standard YOLOv9 decoupled head with adaptive anchor boxes.
6. Dataset and Experimental Setup
I used the PVEL-AD dataset — a large-scale open-world anomaly detection dataset for photovoltaic cell electroluminescence (EL) images. This dataset was jointly constructed by several leading research institutions and contains 36,543 near-infrared images in 16-bit TIFF format. The images cover various real-world production conditions and include eight common defect categories such as cracks, finger interruptions, black cores, thick lines, horizontal defects, and short circuits. The key statistics of the dataset are summarized in the table below:
| Indicator | Value | Technical Feature |
|---|---|---|
| Total samples | 36,543 | EL/IR dual-modal data |
| Defect sample ratio | 42.7% | Balanced distribution across 12 defect types |
| Single sample size | 2048×2048 pixels | Industrial-grade imaging resolution |
| Total bounding boxes | 40,358 | Average 1.1 defects per image |
| Anomaly categories | 8 | Typical defects including cracks, finger breaks, and black cores |
The dataset was split into training, validation, and test sets in a ratio of 10:1:1. The training configurations are shown in the following table:
| Parameter | Training Set | Validation Set | Test Set |
|---|---|---|---|
| Sample size | 36,543 | 3,654 | 3,655 |
| Input size | 2048×2048 | 2048×2048 | 2048×2048 |
| Batch size | 16 | 8 | 8 |
| Optimizer | AdamW | ||
| Learning rate | 1e-4 | ||
All experiments were conducted on the same hardware and software environment, as described below:
| Configuration Item | Value |
|---|---|
| Host | Legion Y9000P IRX9 |
| Operating System | Windows 11 Family 64-bit |
| CPU | Intel Core i9-14900HX @ 5.80 GHz |
| GPU | NVIDIA GeForce RTX 4080 SUPER (16 GB) |
| Python | 3.10.14 |
| PyTorch | 2.2.2 |
| CUDA | 10.2 |
During training, I set the batch size to 16, the number of epochs to 200, the input image size to 2048×2048, and the initial learning rate to 0.001. The SGD optimizer with a momentum of 0.9 was used. I trained all models from scratch to ensure a fair comparison and to evaluate the intrinsic capability of each architecture. Model weights were saved every 10 epochs.
7. Results and Analysis
7.1 Comparative Performance of YOLOv9-SPDEMA
I compared YOLOv9-SPDEMA against the baseline YOLOv9 and several other state-of-the-art detectors, including YOLOv5, YOLOv8, PVT-YOLOv5, and Faster R-CNN. The evaluation metric was the mAP@(50-95) as well as class-wise AP values for the six fault categories. The results are presented below:
| Algorithm | mAP(50-95) | Crack | Finger | Black_core | Thick_line | Horizontal | Short_circuit |
|---|---|---|---|---|---|---|---|
| YOLOv5 | 0.623 | 0.431 | 0.524 | 0.954 | 0.446 | 0.352 | 0.979 |
| YOLOv8 | 0.621 | 0.491 | 0.537 | 0.960 | 0.468 | 0.379 | 0.979 |
| PVT-YOLOv5 | 0.574 | 0.484 | 0.522 | 0.956 | 0.474 | 0.332 | 0.981 |
| Faster R-CNN | 0.633 | 0.481 | 0.544 | 0.963 | 0.467 | 0.355 | 0.980 |
| YOLOv9 | 0.634 | 0.494 | 0.548 | 0.958 | 0.479 | 0.391 | 0.982 |
| YOLOv9-SPDEMA | 0.657 | 0.527 | 0.567 | 0.969 | 0.491 | 0.405 | 0.984 |
From the experimental results, my proposed YOLOv9-SPDEMA algorithm achieved an mAP@(50-95) of 65.7%, surpassing the baseline YOLOv9 by 3.6 percentage points. Compared to the PVT-YOLOv5 algorithm, the improvement was even more significant — 14.5% relative improvement. Particularly for the three small-target fault categories (Crack, Finger, and Thick_line), the AP improvements over the baseline YOLOv9 were 6.7%, 2.5%, and 3.4%, respectively. This confirms that the SPDConv+EMA optimization effectively addresses the problem of fine-grained information loss during feature extraction.
7.2 Visual Comparison for Small-Target Faults
I performed qualitative comparisons on representative infrared images containing Finger, Crack, and Thick_line faults. For the Finger fault type, the test image contained 27 labeled instances. The YOLOv9-SPDEMA model achieved the lowest number of missed and false detections — only 2 missed detections and 0 false positives. In comparison, YOLOv5 and Faster R-CNN exhibited 4 missed detections and multiple false positives. The EMA attention mechanism was instrumental in suppressing background noise and focusing on the relevant fault regions, thereby increasing the confidence of detections.
In the Crack fault case, where the infrared features are relatively more distinct than other fault types, YOLOv9-SPDEMA achieved 0 missed detections and 0 false alarms. This result highlights the benefit of the improved convolution module, which retains edge information that is critical for detecting thin cracks.
The most challenging scenario was the Thick_line fault, where the fault appears as a small black patch near the busbar or grid lines. Here, all competing algorithms exhibited some degree of miss or false detection. YOLOv8 had 7 missed regions and 2 false alarms, while YOLOv9-SPDEMA achieved the fewest errors, demonstrating the strengthened ability of the EMA mechanism to correlate global and local features under ambiguous conditions.
7.3 Ablation Study of YOLOv9-SPDEMA
To evaluate the individual contribution of SPDConv and EMA, I conducted ablation experiments. The results are shown in the following table:
| Algorithm | mAP(50-95) | Crack | Finger | Black_core | Thick_line | Horizontal | Short_circuit |
|---|---|---|---|---|---|---|---|
| YOLOv9 | 0.634 | 0.494 | 0.548 | 0.958 | 0.479 | 0.391 | 0.982 |
| YOLOv9 + SPDConv (auto) | 0.648 | 0.506 | 0.564 | 0.968 | 0.485 | 0.397 | 0.971 |
| YOLOv9-SPDEMA | 0.657 | 0.527 | 0.567 | 0.969 | 0.491 | 0.405 | 0.984 |
In the absence of SPDConv, the mAP@(50-95) decreased by 1.4 percentage points, confirming that SPDConv plays a crucial role in preserving small-target details. Without EMA, the model’s training stability worsened, as evidenced by larger loss fluctuations (±5% compared to ±2% with EMA), and the mAP dropped by about 0.9 percentage points. When both modules are removed, the mAP@(50-95) fell by 2.3 percentage points, and the loss fluctuation further increased. This demonstrates the synergistic effect of both modules in improving detection performance and training robustness.
7.4 Performance of YOLOv9-AIFIDS
I evaluated the YOLOv9-AIFIDS algorithm on the same dataset and compared it with the baseline YOLOv9 and YOLOv9-SPDEMA. The quantitative results are presented below:
| Algorithm | mAP(50-95) | Crack | Finger | Black_core | Thick_line | Horizontal | Short_circuit |
|---|---|---|---|---|---|---|---|
| YOLOv5 | 0.623 | 0.431 | 0.524 | 0.954 | 0.446 | 0.352 | 0.979 |
| YOLOv8 | 0.621 | 0.491 | 0.537 | 0.960 | 0.468 | 0.379 | 0.979 |
| PVT-YOLOv5 | 0.574 | 0.484 | 0.522 | 0.956 | 0.474 | 0.332 | 0.981 |
| Faster R-CNN | 0.633 | 0.481 | 0.544 | 0.963 | 0.467 | 0.355 | 0.980 |
| YOLOv9 | 0.634 | 0.494 | 0.548 | 0.958 | 0.479 | 0.391 | 0.982 |
| YOLOv9-SPDEMA | 0.657 | 0.527 | 0.567 | 0.969 | 0.491 | 0.405 | 0.984 |
| YOLOv9-AIFIDS | 0.656 | 0.531 | 0.568 | 0.970 | 0.504 | 0.409 | 0.954 |
YOLOv9-AIFIDS achieved an mAP@(50-95) of 65.6%, essentially matching the performance of YOLOv9-SPDEMA, while showing even better results on the three small-target categories. The average mAP for Crack, Finger, and Thick_line under YOLOv9-AIFIDS was 53.4%, which is slightly higher than the 52.8% achieved by YOLOv9-SPDEMA. This indicates that the DySample+AIFI combination is particularly effective at refining the spatial and semantic features needed for small-target detection.
7.5 Ablation Study of YOLOv9-AIFIDS
I performed separate ablation experiments to quantify the contributions of AIFI and DySample. The results are shown below:
| Algorithm | mAP(50-95) | Crack | Finger | Black_core | Thick_line | Horizontal | Short_circuit |
|---|---|---|---|---|---|---|---|
| YOLOv9 | 0.634 | 0.494 | 0.548 | 0.958 | 0.479 | 0.391 | 0.982 |
| YOLOv9 + AIFI (auto) | 0.651 | 0.519 | 0.567 | 0.967 | 0.498 | 0.381 | 0.971 |
| YOLOv9-AIFIDS | 0.656 | 0.531 | 0.568 | 0.970 | 0.504 | 0.409 | 0.954 |
Adding AIFI to YOLOv9 improved the mAP@(50-95) by 1.7 percentage points, primarily by enhancing the semantic features in the S5 layer. Adding DySample on top of AIFI brought a further improvement of 0.5 percentage points. The combination achieved a total improvement of 3.6 percentage points over the baseline. The F1-score also improved to 0.89, confirming a balanced trade-off between precision and recall.
7.6 Small-Target Recall and F1-Curve
In addition to mAP, I evaluated the small-target recall rate (Recall_S) and the F1-curve. The recall improvement for the small-target set is shown in the following comparison:
| Algorithm | Recall_S |
|---|---|
| YOLOv9 | 79.2% |
| YOLOv9 + DySample | 82.4% |
| YOLOv9-AIFIDS | 84.2% |
These results demonstrate that both improvement paths significantly enhance the recall of small targets on solar panels, thereby reducing the risk of missed detections in real-world inspection applications.
8. Discussion of Technical Innovations
The innovations in my work can be categorized into three major aspects: detail-preserving convolution, dynamic sampling, and attention-driven feature interaction. The first path (YOLOv9-SPDEMA) tackles the fundamental problem of downsampling information loss. The SPDConv module provides a lossless way to reduce spatial resolution while increasing channel dimensionality, and the EMA module supplies stable attention weights that adapt to the characteristics of small faults. The second path (YOLOv9-AIFIDS) improves the quality of upsampled feature maps and leverages self-attention to model long-range dependencies. The DySample module adaptively selects sampling points, preserving sharp boundaries, while AIFI enriches the high-level semantics without increasing computational cost significantly.
Both algorithms were designed to complement each other and cover the complete detection pipeline: feature extraction → upsampling → feature interaction → prediction. The experimental results confirm that both optimization paths improve detection accuracy for solar panels under identical conditions. The choice between the two methods depends on the deployment scenario. YOLOv9-SPDEMA is more suitable for edge devices with limited computational resources because it avoids expensive transformer layers. YOLOv9-AIFIDS, on the other hand, offers slightly better performance on extremely small and densely packed faults, making it ideal for high-accuracy offline inspection.
9. Conclusion and Future Work
In this research, I have systematically investigated the problem of small-target fault detection in solar panels using improved YOLOv9 architectures. Two distinct optimization strategies were proposed and validated:
- YOLOv9-SPDEMA: Combines SPDConv for detail-preserving downsampling and EMA for reliable attention weighting. This method improved mAP@(50-95) by 3.6% over the baseline YOLOv9 and demonstrated excellent robustness in low-resolution infrared images.
- YOLOv9-AIFIDS: Combines DySample dynamic upsampling and AIFI intra-scale feature interaction. This method achieved a comparable mAP while delivering the highest performance on the three most challenging small-target fault categories.
The comparative experiments against YOLOv5, YOLOv8, PVT-YOLOv5, and Faster R-CNN show the superiority of both improved algorithms for the detection of microcracks, finger electrode defects, and thick grid line faults on solar panels. Ablation studies further confirmed the individual contributions of each module and the synergistic benefits when combined.
Looking forward, I plan to extend this work in the following directions:
- Dynamic attention with temporal modeling: Introducing a spatio-temporal attention module that can track the evolution of faults over time, enabling early prediction of crack growth or hot spot expansion.
- Incremental learning framework: Building a continuous learning system that can adapt to new fault types and environmental conditions without retraining from scratch, thus improving the long-term maintainability of PV stations.
- Cross-modal transfer learning: Combining ground-level infrared images with satellite remote sensing data to create a more comprehensive monitoring system, where macro-level environmental information helps the model generalize across regions and climates.
- Multi-sensor fusion: Integrating weather sensors, vibration sensors, and power output data with image data to form a multi-dimensional diagnostic platform for solar panels. This would enable not only fault detection but also prognostics of remaining useful life.
In summary, this research contributes to the field of photovoltaic panel inspection by providing effective and efficient deep learning solutions for small-target fault detection. The proposed algorithms show great potential for deployment in real-world solar panel monitoring systems, thereby supporting the reliable and sustainable operation of photovoltaic power plants.