# Lem-Detector: An Efficient Detector for Photovoltaic Panel Defect Detection
## 3 Methods
### Efficient Feature Enhancement Module (EFEM)  
The EFEM comprises four structurally similar blocks. Each block employs Group Convolution with a 7 \(\times\) 7 kernel to process the primary feature information extracted from  
Figure 4: Effects of data augmentation ((**A**): defect overlap; (**B**): small-scale Star crack; (**C**): small-scale Finger; (**D**): crack crossing the busbar). Figure 3: The architecture of the LFA. the image samples, optimizing computational efficiency and reducing memory overhead. Normalization and a ReLU activation function are then applied, enabling the model to capture more complex features and improve convergence speed. To capture global information and establish long-range dependencies, the dimensions of the sample images are adjusted, and global average pooling is applied separately in both the height and width directions. To avoid information mixing, the height and width features are not directly concatenated. Instead, 1D convolutional kernels of \(5\times 5\) are used to extract richer feature information in both directions. Given the limited training samples and potential memory access issues, Group Normalization is employed to normalize feature maps. Group Normalization divides channels into multiple groups, computing the mean and variance within each group. This approach mitigates high false detection rates when using small mini-batches, enhancing model stability. Finally, the processed features are passed through a sigmoid function to select and amplify important features, allowing the model to focus on critical areas and assign appropriate weights to the feature maps. The specific content is shown in Figure 5.  
The input feature information is represented as \(X\in R^{C\times H\times W}\), where \(C\), \(H\), and \(W\) denote the channel, height, and width dimensions. The input \(X\) undergoes Group Convolution, which groups the different feature maps from the input layer. Different convolution kernels are then applied to each group, thereby reducing the computational load of the convolution process. After processing, K groups of convolution information are obtained as \(X_{1}\in R^{CR^{2}\times H\times W}\), where \(K\) represents the number of grouped convolution kernels. After group normalization and ReLU activation, normalizing the input data alleviates the variations in inputs between layers in deep neural networks, resulting in more stable training. The activation function enables the neural network to fit and express more complex features. Next, we adjust the size of \(X_{2}\in R^{C\times KH\times KW}\) for subsequent feature extraction. \(z_{c}^{h}\) and \(z_{c}^{w}\) represent the global average pooling in the vertical and horizontal directions, respectively. Two pooling kernels, sized \(KH\times 1\) and \(1\times KW\), are used to perform one-dimensional feature encoding on the height and width of the input feature map, yielding feature outputs in both directions. The specifics are as follows:  
\[z_{c}^{h}(h)=\frac{1}{H}\sum\
olimits_{0\leq i<H}\ x_{c}(h,i) \tag{2}\]  
\[z_{c}^{w}(w)=\frac{1}{W}\sum\
olimits_{0\leq j<W}\ x_{c}(j,w) \tag{3}\]  
To better capture the global information in the images and enhance the model's ability to focus on critical regions, we designed a more efficient processing method. This approach preserves more small-scale defect information, preventing the mixing of irrelevant information. We processed the height and width positional information extracted from \(z_{c}^{h}\) and \(z_{c}^{w}\) separately. We employed a \(5\times 5\) one-dimensional convolution to enhance the positional  
Figure 5: The architecture of the EFEM. information in both the vertical and horizontal directions. This large kernel convolution effectively processes the height and width directions, allowing for a more detailed capture of spatial information and enhancing feature expression capabilities, while also capturing long-distance spatial dependencies. This approach achieves a balance between detection accuracy and parameter count.