## 2 Materials and Methods
### Object Detection Algorithm
#### 2.2.2 GSConv  
In the architecture of YOLOv5, the Neck component plays a crucial role in fusing the low-level and high-level feature information extracted by the Backbone network. This fusion is achieved through a combination of up-sampling and down-sampling techniques. Primarily, this process is facilitated by a series of standard convolutional operations. The structure of the standard convolutional (Conv) module within the Neck is depicted in Figure 3.  
In standard convolution, each set of convolutional kernels operates on the corresponding channels of the input feature map, with their outputs being element-wise added to produce a single feature map. While this approach achieves the goal of feature fusion, it poses challenges for mobile and embedded devices, which may struggle to run complex deep learning network models. Consequently, in the field of computer vision, there is an ongoing effort to miniaturize neural networks. Currently, this objective is primarily achieved through the use of depth-wise separable convolution (DSConv). DSConv reduces  
Figure 2: YOLOv5 network structure diagram.  
the number of parameters and floating-point operations (FLOPs), making it more feasible for less powerful devices.  
In contrast to standard convolution, depth-wise separable convolution involves each channel of the input feature map convolving with only one kernel, as illustrated in Figure 4. Due to utilizing only a fraction of the parameters compared to standard convolution, this approach significantly reduces the size and computational load of the model. However, in depth-wise separable convolution, each kernel operates on a single channel, resulting in isolated the processing of channel information. This separation means that the feature information between channels is not effectively fused during the computation process.  
To mitigate complexity while retaining as much inter-channel information as possible, we have applied a lightweight modification to the standard convolution structure. This involves the introduction of a lightweight convolution method, GSConv (grouped separable convolution), to enhance the standard convolution (Conv) within the CBS module of the Neck layer. The structure of the GSConv module is depicted in Figure 5. This adaptation not only lightens the computational load but also preserves the crucial interplay of features across different channels, a balance crucial for the efficiency and effectiveness of the model.  
Figure 4: DSConv module structure.  
Figure 3: Conv module structure.  
In GSConv, the input feature map initially undergoes a 1 \(\times\) 1 standard convolution, compressing the number of channels. This is followed by a 5 \(\times\) 5 depth-wise convolution with a stride of 1, maintaining the output channel count at half of the original number. The outputs of the standard and depth-wise convolutions are then concatenated. Subsequently, a shuffle operation is applied to deeply blend the information generated by both processes. GSConv harnesses the rich feature extraction of standard convolution and the resource efficiency of depth-wise convolution, offering significant advantages when deployed on edge devices compared to traditional convolution. The adoption of GSConv effectively minimizes the drawbacks of DSConv while capitalizing on its strengths.