# Dark-YOLO: A Low-Light Object Detection Algorithm Integrating Multiple Attention Mechanisms
## 2 Method
### Dynamic Feature Extraction Module (PSM)  
Traditional convolutional networks assume that the input image contains complete and uniformly distributed information. However, under low-light conditions, this assumption no longer holds due to the presence of increased noise, reduced contrast, and the loss of fine details. These issues lead to incomplete image features, which in turn adversely affect  
Figure 3: Structure diagram of the CSPPF model. The CSPPF module consists of two parallel pathways: one pathway includes two max pooling layers followed by an average pooling layer in series, while the other pathway includes two average pooling layers followed by a max pooling layer. By employing parallel max pooling and average pooling operations, the module extracts important features at different hierarchical levels. These extracted features are then concatenated and further processed by a convolutional layer. This approach enhances feature diversity, making it particularly effective for extracting key information from complex backgrounds. the model's accuracy in detecting target objects. To address these challenges, we propose a dynamic feature extraction module (PSM), designed to enhance the model's ability to extract effective information, particularly from regions where inadequate illumination results in limited useful data availability. In low-light conditions, partial convolution (PConv) processes only valid pixels to reduce noise interference. The SimAM attention mechanism dynamically suppresses irrelevant regions (such as dark backgrounds) and focuses on object contours. The architecture of the PSM module is illustrated in Figure 4.  
PSM integrates a parameter-free attention mechanism with partial convolution. Unlike existing channel/spatial attention modules, the parameter-free attention mechanism of PSM derives 3D attention weights for the feature maps without the need for additional parameters, focusing on neurons containing important information. This mechanism enhances the model's ability to localize detection targets in low-light environments. The attention weights are computed based on the local self-similarity of the image by evaluating the similarity between pixels, which directs the model's focus towards the objects of interest [39]. The specific process is defined in Equations (9) and (10), where \(\lambda\) represents the regularization term, \(t_{i}\) is the \(i\)th neuron in a single channel of the input feature map, \(\hat{\mu}\) represents the mean of all neurons within a single channel, \(\hat{\sigma}^{2}\) is the variance of all neurons in the single channel, and \(e_{i}^{*}\) is the energy function, which generates attention weights through the sigmoid function. \[e_{i}^{*}=4(\hat{\sigma}+\lambda)/[(t_{i}-\hat{\mu})^{2}+2\hat{\sigma}^{2}+2\lambda] \tag{9}\]  
\[X^{\prime}=sigmoid(1/e_{i}^{*})\cdot X \tag{10}\]  
Partial convolution allows the convolution kernel to operate only on the valid pixels within its receptive field, thereby preventing information distortion caused by invalid or missing data. Given the substantial loss of data in low-light images, partial convolution  
Figure 4: Structure diagram of the PSM model. The PSM module begins with a two-dimensional convolution to perform initial feature extraction. The extracted feature maps are then split and fed into different branches. The parameter-free attention mechanism, SimAM, is integrated to help the network better focus on important features. Subsequently, a partial convolution module is employed to further refine the features, and finally, a convolutional layer is used to fuse all branches into a unified representation. reduces computational cost, enhances robustness against missing information, and improves feature extraction under low-light conditions. As a novel implementation of partial convolution, it applies the traditional convolution operation to only a small subset of input channels while leaving the remaining channels unchanged, effectively reducing computational load and memory access to some extent [40].