1. Introduction
The global energy landscape is undergoing a profound transformation as nations strive to meet ambitious carbon neutrality targets. Among the various renewable energy sources, photovoltaic power generation has emerged as a cornerstone of clean energy infrastructure, with polycrystalline silicon solar panels playing a pivotal role in converting sunlight into electricity. The increasing deployment of solar panels across diverse geographical regions—from vast desert installations to urban rooftop arrays—has created an urgent need for robust, efficient, and automated inspection methodologies.
Throughout my research, I have focused on addressing the critical challenges associated with surface defect detection in polycrystalline silicon solar panels. These challenges arise in two distinct operational contexts: manufacturing production lines, where electroluminescence imaging is employed to identify internal structural defects, and outdoor operational environments, where visible light imaging is used to detect surface-level anomalies. The traditional approaches to defect detection in solar panels have relied heavily on manual inspection, which suffers from subjectivity, low efficiency, and inconsistent accuracy. As the scale of photovoltaic installations continues to expand, the limitations of manual inspection become increasingly untenable.
The primary objective of my research has been to develop advanced deep learning-based methodologies that can deliver high-precision, real-time defect detection for polycrystalline silicon solar panels across both manufacturing and operational scenarios. This paper presents a comprehensive account of my research journey, detailing the development, evaluation, and practical implementation of two novel detection frameworks: EVT-Net and MCAM-Net.
2. Background and Related Work
2.1 Defect Types in Solar Panels
Polycrystalline silicon solar panels are subject to various defect categories that emerge during different lifecycle stages. During manufacturing, defects such as black cores, micro-cracks, finger interruptions, horizontal dislocations, short circuits, and thick lines can occur. These internal defects compromise the electrical integrity of the solar panels, reducing their energy conversion efficiency. In outdoor operational environments, solar panels accumulate dust, bird droppings, snow coverage, and may sustain physical or electrical damage. These operational defects significantly impact the power output and long-term reliability of photovoltaic systems.
The comprehensive detection of both internal and external defects in solar panels requires a multi-modal approach. Electroluminescence imaging provides detailed visualization of internal crystalline structure and electrical pathways, revealing subtle anomalies that are invisible to conventional imaging. Visible light imaging, on the other hand, captures surface conditions directly, enabling the identification of environmental and mechanical damage.
2.2 Evolution of Defect Detection Methods
The evolution of defect detection techniques for solar panels has progressed through three major phases: manual inspection, traditional computer vision approaches, and deep learning-based methodologies. Manual inspection, while flexible, suffers from inherent subjectivity and limited throughput. Traditional image processing techniques—including threshold-based segmentation, edge detection, and frequency domain analysis—offered partial automation but struggled with the complex textures and varying illumination conditions characteristic of solar panel imagery.
Deep learning has fundamentally transformed the defect detection landscape. Convolutional neural networks enable automatic feature learning from large-scale datasets, eliminating the need for hand-crafted feature extraction. More recently, vision Transformers have introduced powerful global attention mechanisms that capture long-range dependencies, offering complementary strengths to convolutional approaches. My research builds upon these advances while addressing specific challenges unique to solar panel defect detection.
3. Mathematical Foundations
3.1 Convolutional Neural Networks
The convolutional operation serves as the fundamental building block of modern defect detection architectures. The discrete convolution operation can be formally expressed as:
$$Y_{i,j,c’} = \sum_{m=0}^{k_h-1} \sum_{n=0}^{k_w-1} \sum_{d=0}^{C_{in}-1} X_{i+m, j+n, d} \cdot K_{m,n,d,c’}$$
where $X \in \mathbb{R}^{H \times W \times C_{in}}$ represents the input feature map, $K \in \mathbb{R}^{k_h \times k_w \times C_{in} \times C_{out}}$ denotes the convolutional kernel, and $Y \in \mathbb{R}^{H’ \times W’ \times C_{out}}$ is the output tensor.
3.2 Self-Attention Mechanisms
The self-attention mechanism, central to Transformer-based architectures, computes attention weights through the scaled dot-product operation:
$$\text{Attention}(Q, K, V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V$$
where $Q$, $K$, and $V$ represent the query, key, and value matrices, respectively, and $d_k$ denotes the dimension of the key vectors. The quadratic complexity of this operation, $\mathcal{O}(N^2)$ where $N$ represents the number of tokens, poses significant computational challenges for real-time detection.
4. EVT-Net for Electroluminescence Defect Detection
4.1 Overview
The detection of defects in electroluminescence images of polycrystalline silicon solar panels presents unique challenges. These images exhibit low contrast between defective and non-defective regions, complex crystalline textures that mimic defect patterns, and extremely small-scale defects that are difficult to localize. To address these challenges, I developed EVT-Net (Efficient Vision Transformer Network), a specialized detection architecture that combines the strengths of Transformer-based global modeling with efficient convolutional processing.
4.2 M-CASViT Backbone Network
The backbone network plays a crucial role in extracting meaningful features from input images. I modified the CAS-ViT architecture to create M-CASViT, optimizing both its structural configuration and down-sampling strategy for enhanced defect feature extraction.
Stage Layer Optimization: The computational complexity of Transformer blocks is governed by:
$$\mathcal{O}(hwC + h^2w^2C)$$
where $h$ and $w$ denote spatial dimensions and $C$ represents the channel count. Recognizing that early network stages process larger feature maps, I systematically evaluated various layer configurations to achieve an optimal balance between detection accuracy and computational efficiency. The experimental results comparing different configurations are presented in the following table:
| Blocks | Params (M) | FLOPs (G) | mAP50 (%) | mAP50-95 (%) |
|---|---|---|---|---|
| [2, 2, 4, 2] | 8.19 | 28.98 | 92.72 | 66.69 |
| [2, 2, 2, 2] | 7.40 | 26.78 | 92.63 | 65.71 |
| [1, 1, 1, 1] | 5.87 | 21.29 | 91.62 | 63.84 |
| [1, 1, 2, 2] | 7.26 | 23.09 | 92.76 | 66.75 |
| [1, 1, 2, 1] | 6.26 | 22.39 | 92.75 | 66.73 |
The optimal configuration [1, 1, 2, 1] reduces parameters by 1.93M and computational cost by 6.59G compared to the original architecture while simultaneously improving detection accuracy.
Multi-branch Stem Module: Traditional down-sampling in the CAS-ViT architecture relies on simple strided convolutions, which risk losing critical spatial details. I designed a Multi-branch Stem (M-Stem) module that processes features through three parallel paths:
$$Y_{\text{conv}} = f_{3\times3}^{s=2}(X)$$
$$B_{\text{conv}} = f_{3\times3}^{s=2}(Y_{\text{conv}}), \quad B_{\text{avg}} = P_{\text{avg}}^{s=2}(Y_{\text{conv}}), \quad B_{\text{max}} = P_{\text{max}}^{s=2}(Y_{\text{conv}})$$
The outputs from all branches are concatenated and fused through a $1\times1$ convolution:
$$Z = f_{1\times1}^{\text{conv}}\left(\text{Concat}(B_{\text{conv}}, B_{\text{avg}}, B_{\text{max}})\right)$$
The ablative analysis confirmed the effectiveness of this design:
| Module | P (%) | R (%) | Params (M) | FLOPs (G) | mAP50 (%) | mAP50-95 (%) |
|---|---|---|---|---|---|---|
| Stem | 91.42 | 91.13 | 6.27 | 22.54 | 91.73 | 66.25 |
| M-Stem | 92.37 | 93.27 | 6.26 | 22.39 | 92.75 | 66.73 |
4.3 Dual-Path Dynamic Calibration Feature Pyramid Network
To enhance the multi-scale feature fusion capability of the detection framework, I constructed the Dual-path Dynamic Calibration Feature Pyramid Network (DDC-FPN). This architecture employs a Dynamic Dual-Path Calibration Block (DDC Block) that processes channel and spatial information through parallel pathways.
The channel pathway computes attention weights through:
$$A_c = \sigma(W_2 \delta(W_1 \text{GAP}(X)))$$
where $\sigma$ denotes the sigmoid function, $\delta$ represents ReLU activation, and $W_1$, $W_2$ are fully connected layer weights. The spatial pathway extracts contextual features through lightweight convolutions:
$$A_s = \sigma(C_{1\times1}^{(2)}(\text{SiLU}(GN(C_{1\times1}^{(1)}(X)))))$$
A dynamic gating unit computes adaptive fusion weights:
$$G = \sigma(C_{1\times1}^{(2)} \text{ReLU}(C_{1\times1}^{(1)}(\text{GAP}(X))))$$
The final output is produced through residual connection:
$$X_{\text{out}} = X + G \odot A_c + (1-G) \odot A_s$$
The experimental validation showed substantial improvements:
| Model | P (%) | R (%) | Params (M) | FLOPs (G) | mAP50 (%) | mAP50-95 (%) |
|---|---|---|---|---|---|---|
| FPN | 92.11 | 91.89 | 6.19 | 22.39 | 91.19 | 65.21 |
| DDC-FPN | 92.37 | 93.27 | 6.26 | 22.39 | 92.75 | 66.73 |
4.4 Decoupled Detection Head
Traditional coupled detection heads share features between classification and regression tasks, leading to gradient conflicts and suboptimal performance. I adopted a Decoupled Head architecture that separates these tasks into independent branches. The total loss function integrates multiple components:
$$\mathcal{L} = \lambda_{\text{reg}}\mathcal{L}_{\text{reg}} + \mathcal{L}_{\text{obj}} + \mathcal{L}_{\text{cls}} + \mathbb{1}_{l1}\mathcal{L}_{l1}$$
The regression loss employs IoU-based computation:
$$\mathcal{L}_{\text{reg}} = \frac{1}{N_{\text{pos}}} \sum_{i \in \Omega_{\text{pos}}} \left[1 – \text{IoU}(B_{\text{pred}}^i, B_{\text{gt}}^i)\right]$$
The objectness and classification losses use binary cross-entropy formulations:
$$\mathcal{L}_{\text{obj}} = \frac{1}{N_{\text{all}}} \sum_{i=1}^{N_{\text{all}}} \left[o_i^* \log \sigma(o_i) + (1-o_i^*) \log(1-\sigma(o_i))\right]$$
The experimental comparison demonstrated significant improvements:
| Head Type | P (%) | R (%) | Params (M) | FLOPs (G) | mAP50 (%) | mAP50-95 (%) |
|---|---|---|---|---|---|---|
| Coupled | 91.51 | 92.43 | 6.26 | 22.39 | 90.35 | 64.17 |
| Decoupled | 92.37 | 93.27 | 6.26 | 22.39 | 92.75 | 66.73 |
4.5 EL2021 Dataset and Results
My experiments utilized the EL2021 dataset, containing electroluminescence images of solar panels with six defect categories: black_core, crack, finger, horizontal_dislocation, short_circuit, and thick_line. After addressing annotation quality issues through careful re-annotation, I divided the dataset into training, validation, and test sets following a 7:2:1 ratio.
The comprehensive comparison of EVT-Net against state-of-the-art detection models yielded the following results:
| Model | P (%) | R (%) | Params (M) | FLOPs (G) | mAP50 (%) | mAP50-95 (%) |
|---|---|---|---|---|---|---|
| Faster R-CNN | 92.32 | 91.95 | 41.25 | 91.03 | 91.83 | 66.41 |
| FCOS | 89.71 | 88.06 | 31.85 | 78.73 | 88.92 | 63.87 |
| RetinaNet | 87.95 | 89.47 | 36.21 | 82.57 | 87.21 | 61.83 |
| RT-DETR | 88.24 | 90.33 | 19.87 | 56.90 | 89.75 | 64.56 |
| YOLOv8n | 90.56 | 91.71 | 3.01 | 8.20 | 90.83 | 65.84 |
| YOLOv9t | 92.89 | 90.02 | 2.01 | 7.90 | 91.15 | 66.21 |
| YOLOv10n | 92.27 | 90.45 | 2.71 | 8.40 | 91.58 | 66.75 |
| YOLO11n | 90.95 | 91.12 | 2.59 | 6.40 | 91.97 | 65.02 |
| YOLOv12n | 91.31 | 92.64 | 2.60 | 6.50 | 90.67 | 65.59 |
| EVT-Net | 92.37 | 93.27 | 6.26 | 22.39 | 92.75 | 66.73 |
The results demonstrate that EVT-Net achieves state-of-the-art performance in detecting electroluminescence defects in polycrystalline silicon solar panels while maintaining reasonable computational efficiency.
5. MCAM-Net for Visible Light Defect Detection
5.1 Overview
For outdoor operational inspection of solar panels, visible light imaging provides practical advantages over electroluminescence techniques. However, visible light images of solar panels present their own challenges, including variable defect shapes, multi-scale defect sizes, and low contrast between defects and backgrounds under varying illumination conditions. I developed MCAM-Net (Multi-scale Convolutional Attention Mechanism Network) based on the YOLO11 architecture to address these challenges.
5.2 Channel Prioritized Multi-scale Subspace Attention
The CPMSSA attention mechanism represents a key innovation in my research. It combines channel attention with multi-scale subspace processing to enhance the model’s sensitivity to subtle defect features. The overall attention computation follows:
$$X_c = \text{CA}(X) \odot X$$
$$Y = \text{MSSA}(X_c)$$
The channel attention component adopts the CBAM-style computation:
$$M_c = \sigma(\text{MLP}(\text{AvgPool}(F)) + \text{MLP}(\text{MaxPool}(F)))$$
The multi-scale subspace attention processes features through parallel branches:
$$\text{MSAM}(X) = \sum_{k=1}^{K} \left[\text{Concat}\left(\text{ReLU}\left(D_k^{5\times5}(X_i)\right)\right)\right] \odot X + X$$
The effectiveness comparison against alternative attention mechanisms is presented below:
| Model | P (%) | R (%) | Params (M) | FLOPs (G) | mAP50 (%) | mAP50-95 (%) |
|---|---|---|---|---|---|---|
| YOLO11n | 70.93 | 71.22 | 2.59 | 6.40 | 71.82 | 45.93 |
| + LRSA | 69.83 | 70.15 | 2.54 | 6.60 | 71.08 | 46.03 |
| + SEA | 72.13 | 71.29 | 2.60 | 6.40 | 72.73 | 45.29 |
| + MSCA | 71.37 | 70.43 | 2.68 | 6.70 | 72.29 | 46.11 |
| + CPMSSA | 72.58 | 71.51 | 2.49 | 6.30 | 73.31 | 47.27 |
5.3 C2f_MSCBlock Module
The C2f_MSCBlock module was designed to enhance multi-scale feature extraction capabilities. The foundation of this design is the MSCBlock module, which employs depthwise strip convolution decomposition to achieve an equivalent large receptive field with minimal parameter overhead:
$$\text{C2f\_MSCBlock}(X) = \text{Conv}_{1\times1}\left(\text{BN}\left(\text{Concat}\left(\text{DW}_{7\times1}(\text{DW}_{1\times7}(Y_1))\right)\right)\right) + \text{Input}$$
The parameter efficiency of this approach is evident in the comparison:
$$7 \times 7 \text{ conv: } 49C^2 \text{ parameters}; \quad \text{MSCBlock: } 7C + 7C = 14C \text{ parameters}$$
This represents a reduction of more than two orders of magnitude. The experimental results confirmed the effectiveness:
| Model | P (%) | R (%) | Params (M) | FLOPs (G) | mAP50 (%) | mAP50-95 (%) |
|---|---|---|---|---|---|---|
| YOLO11n | 70.93 | 71.22 | 2.59 | 6.40 | 71.82 | 45.93 |
| + C2f_DHSA | 69.83 | 70.15 | 2.89 | 7.00 | 70.35 | 44.29 |
| + C2f_SFHF | 72.13 | 71.29 | 3.11 | 6.90 | 73.82 | 46.33 |
| + C3k2_SSA | 71.37 | 70.43 | 2.81 | 7.10 | 73.69 | 45.98 |
| + C2f_MSCBlock | 72.58 | 71.51 | 2.49 | 6.30 | 74.63 | 46.37 |
5.4 AKConv Deformable Convolution
To handle defects with varying shapes and aspect ratios in solar panels, I integrated AKConv, an arbitrary kernel convolution module that adaptively adjusts sampling positions based on the input content:
$$\Delta P = f_{\text{conv}}^P(X) \in \mathbb{R}^{N \times H’ \times W’}$$
$$P = P_0 + P_n + \Delta P$$
The re-sampled features employ bilinear interpolation for non-integer coordinates:
$$X_{\text{offset}} = X(q_{lt}) \cdot g_{lt} + X(q_{rb}) \cdot g_{rb} + X(q_{lb}) \cdot g_{lb} + X(q_{rt}) \cdot g_{rt}$$
The experimental comparison validated the benefits of this approach:
| Model | P (%) | R (%) | Params (M) | FLOPs (G) | mAP50 (%) | mAP50-95 (%) |
|---|---|---|---|---|---|---|
| YOLOv11n | 70.93 | 71.22 | 2.59 | 6.40 | 71.82 | 45.93 |
| + SPConv | 69.83 | 70.15 | 2.52 | 6.40 | 73.79 | 46.47 |
| + PSConv | 72.13 | 71.29 | 2.52 | 6.40 | 73.90 | 47.23 |
| + FDConv | 71.37 | 70.43 | 2.62 | 6.20 | 74.81 | 46.77 |
| + AKConv | 72.58 | 71.51 | 2.48 | 6.30 | 74.83 | 48.51 |
5.5 Ablation Study
The comprehensive ablation study systematically evaluated the contribution of each improvement module:
| CPMSSA | C2f_MSCBlock | AKConv | Params (M) | FLOPs (G) | mAP50 (%) | mAP50-95 (%) |
|---|---|---|---|---|---|---|
| 2.59 | 6.40 | 71.82 | 45.93 | |||
| ✓ | 2.49 | 6.30 | 73.31 | 47.27 | ||
| ✓ | 2.42 | 6.00 | 74.63 | 46.37 | ||
| ✓ | 2.48 | 6.30 | 74.83 | 48.51 | ||
| ✓ | ✓ | 2.38 | 6.10 | 75.71 | 48.39 | |
| ✓ | ✓ | 2.32 | 5.90 | 76.29 | 47.31 | |
| ✓ | ✓ | 2.31 | 5.90 | 74.28 | 48.35 | |
| ✓ | ✓ | ✓ | 2.21 | 5.70 | 77.34 | 48.91 |
The full combination of all three modules achieved the best performance, reducing parameters by 0.38M and improving mAP50 by 5.52% compared to the baseline.
5.6 MS2025 Dataset and Comparative Results
My evaluation dataset, MS2025, combined real-world images collected from a distributed photovoltaic station with publicly available data. The dataset contains six categories including Dusty, Bird Drop, Physical Damage, Snow, Electrical Damage, and Non Defective samples, reflecting realistic outdoor operating conditions for solar panels.
The comparative evaluation against state-of-the-art models demonstrated the superiority of MCAM-Net:
| Model | P (%) | R (%) | Params (M) | FLOPs (G) | mAP50 (%) | mAP50-95 (%) |
|---|---|---|---|---|---|---|
| Faster R-CNN | 76.12 | 75.45 | 41.25 | 91.03 | 75.92 | 46.11 |
| FCOS | 73.28 | 72.63 | 31.85 | 78.73 | 72.82 | 44.31 |
| RetinaNet | 72.85 | 74.24 | 36.21 | 82.57 | 73.67 | 46.27 |
| RT-DETR | 73.16 | 75.52 | 19.87 | 56.90 | 74.15 | 48.33 |
| YOLOv8n | 75.95 | 74.32 | 3.01 | 8.20 | 74.29 | 46.23 |
| YOLOv9t | 75.63 | 75.98 | 2.01 | 7.90 | 73.73 | 47.38 |
| YOLOv10n | 74.03 | 76.36 | 2.71 | 8.40 | 74.18 | 46.29 |
| YOLO11n | 75.58 | 74.94 | 2.59 | 6.40 | 73.82 | 45.93 |
| YOLOv12n | 75.87 | 74.25 | 2.60 | 6.50 | 74.93 | 47.21 |
| MCAM-Net | 77.85 | 77.20 | 2.21 | 5.70 | 77.34 | 48.91 |
The results confirmed that MCAM-Net achieves a favorable balance between detection accuracy and computational efficiency, making it suitable for real-time outdoor inspection applications.
5.7 Generalization Experiments
To evaluate the generalization capability of MCAM-Net, I conducted additional experiments on the EL2021 electroluminescence dataset. The results were particularly encouraging:
| Model | P (%) | R (%) | Params (M) | FLOPs (G) | mAP50 (%) | mAP50-95 (%) |
|---|---|---|---|---|---|---|
| YOLO11n | 92.95 | 91.12 | 2.59 | 6.40 | 90.24 | 65.02 |
| EVT-Net | 92.37 | 93.27 | 6.26 | 22.39 | 92.75 | 66.73 |
| MCAM-Net | 93.16 | 93.59 | 2.21 | 5.70 | 93.76 | 67.25 |
MCAM-Net achieved superior performance on the electroluminescence dataset despite having significantly fewer parameters and lower computational cost than EVT-Net. These findings demonstrate that MCAM-Net can serve as a universal model for detecting defects in solar panels across both manufacturing and operational scenarios. The training convergence curves exhibited rapid initial loss reduction with stable convergence after approximately 120 epochs, confirming the effectiveness of the model design.
6. System Implementation
6.1 System Architecture
Based on the successful validation of MCAM-Net, I designed and implemented a comprehensive defect detection system for polycrystalline silicon solar panels. The system leverages the generalization capability of MCAM-Net to handle both electroluminescence and visible light imagery. The system architecture follows a modular design pattern, facilitating deployment flexibility and future extension.
The core system components include a user authentication module, image and video input handlers, model inference engine, result visualization interfaces, and data persistence capabilities. The workflow begins with model weight selection, which the user customizes based on the specific inspection scenario for solar panels. Users can then choose between single-frame image detection or continuous video stream analysis.
6.2 Interface Design
The user login interface implements role-based access control, distinguishing between standard users and administrators. The system incorporates complete user management capabilities including registration, password recovery, and secure authentication. The main detection interface provides intuitive controls for model selection, input source configuration, and detection parameter adjustment.
The detection results are displayed with detailed annotations including bounding boxes, class labels, and confidence scores for defects in solar panels. The system supports exporting detection outcomes to CSV format for downstream analysis and reporting.
6.3 Functional Testing
I conducted comprehensive functional testing of the detection system using both image and video inputs. For electroluminescence defect detection, the system successfully identified subtle cracks and other internal defects in solar panels. For visible light image analysis, the system accurately detected physical damage, dust accumulation, and other surface-level anomalies.
Video-based testing was performed using footage collected from a rooftop distributed photovoltaic station. The system demonstrated stable frame-by-frame detection performance, correctly recognizing non-defective solar panels without producing false alarms. This verification confirmed the system’s practical readiness for real-world photovoltaic inspection workflows.
7. Discussion and Future Directions
My research has produced two complementary detection frameworks for solar panels defects. EVT-Net excels at identifying internal structural defects through electroluminescence imaging, while MCAM-Net provides versatile detection capabilities across both imaging modalities with superior computational efficiency. The generalization experiment demonstrated that a single model (MCAM-Net) can effectively handle diverse defect detection tasks for solar panels, simplifying the deployment of automated inspection systems.
The advancements made in this research hold practical significance for large-scale photovoltaic systems. Automated defect detection reduces reliance on subjective human judgment, enabling consistent and scalable quality assurance across both manufacturing and operational phases. Early detection of defects such as micro-cracks and hot spots can prevent catastrophic failures, thereby extending the operational lifetime of solar panels and improving the return on investment of photovoltaic installations.
Despite these advances, several challenges remain. The current detection model was trained on a limited dataset, and its performance under extreme weather conditions warrants further investigation. Multi-modal data fusion emerges as a promising direction, integrating information from visible light, electroluminescence, and infrared thermal imaging to develop a more comprehensive defect profiling system. Domain adaptation and meta-learning techniques are also under consideration to enhance model adaptability to diverse environmental conditions.
A particularly compelling avenue is transitioning from static defect detection to progressive defect prediction. By incorporating temporal analysis and physical degradation models, future systems could forecast defect evolution and estimate the remaining useful life of solar panels, transforming reactive maintenance into predictive maintenance strategies.
8. Conclusion
This paper has presented an in-depth account of my research on advanced surface defect detection algorithms for polycrystalline silicon solar panels. I developed two distinct frameworks targeting different inspection scenarios: EVT-Net for manufacturing line electroluminescence defect detection and MCAM-Net for both manufacturing and outdoor operational visible light inspection. EVT-Net achieves high-precision detection of low-contrast, small-scale internal defects through the M-CASViT backbone, DDC-FPN feature fusion, and Decoupled Head design. MCAM-Net, with its CPMSSA attention mechanism, C2f_MSCBlock module, and AKConv deformable convolution, delivers excellent detection accuracy while maintaining minimal computational overhead. Beyond algorithmic development, I implemented a functional inspection system that integrates these advances into a user-friendly platform. The comprehensive experimental evaluation confirms that my proposed methods significantly improve the accuracy and efficiency of defect detection in solar panels, contributing to the reliable and efficient operation of photovoltaic systems worldwide.