## 3 Algorithm Improvement
### Selection of Lightweight Convolutional Network
#### 3.2.2 ShuffleNet Network  
Research on image classification methods based on lightweight deep neural networks has made considerable progress in recent years. Among them, the ShuffleNet series algorithms proposed by Megvii Technology have been widely applied in object detection for edge computing due to their ability to achieve the best model accuracy with limited computational resources. The ShuffleNet network employs two core operations [22]: group convolution and channel shuffle. These operations substantially decrease the model's computational intricacy while preserving accuracy. However, group convolution is limited in that each group operates independently without feature fusion between groups. As shown in Figure 4a, when the convolution kernel is divided into three groups, the resulting feature maps are also divided into three groups, with each group only exchanging information internally, lacking any information fusion between groups. Therefore, ShuffleNetv1 proposed the concept of channel shuffle, which divides the channels in the feature map into several groups according to certain rules and then rearranges the elements within each group. Doing so enhances the information interaction and fusion between channels, increasing the model's non-linear expression capability without compromising network accuracy. The channel shuffle operation is illustrated in Figure 4b.  
However, ShuffleNetv1 utilizes many grouped and pointwise convolutions, which can slow down the model speed. To address this issue, [PERSON] et al. proposed four design principles for effective and compact networks based on the ShuffleNetv1 model and introduced the ShuffleNetv2 network [23]. A new channel split operation is proposed in ShuffleNetv2, where the network mainly comprises a basic unit and a downsample unit.  
Figure 5a shows the basic unit feature extraction operation with Stride = 1. The input features are evenly divided under the channel split operation; that is, the number of feature channels in each branch accounts for 1/2 of the original number of channels. Then, the left pathway does not perform any processing identity mapping, and the right pathway performs two ordinary convolutions (1 \(\times\) 1) and 1 DWConv (3 \(\times\) 3. Step size is 1) operation, and finally, the left and right branches output feature maps through feature stitching and channel ShuffleNet operations. Figure 5b shows the downsampling operation with Stride = 2. Unlike the basic unit, the downsampling unit does not use channel split without increasing the computational complexity of the network model. Instead, it directly augments both the network's channel count and its overall width, further enhancing the network's ability to extract features. Firstly, the feature map is fed into two branches, each of which undergoes ordinary convolution (1 \(\times\) 1) and DWConv (3 \(\times\) 3. Step size is 2), and after the two branches are concatenated and merged through channels, the quantity of the output channels increases to twice the original, and channel ShuffleNet is performed on the merged feature map.  
Figure 4: Channel shuffling structure diagram. (**a**) Ordinary group convolution; (**b**) group convolution with channel shuffling.  
Within the network architecture of ShuffleNetv2, the number of channels is doubled every time a downsampling operation is performed. With the doubling of the number of channels, the network does not pay attention to the feature channels that significantly impact the classification results. Important and unimportant feature channels have the same weight, resulting in excessive retention of interference information from the mine, which can easily affect the classification effect of non-coal foreign objects. Therefore, this paper chooses the lightweight ShuffleNetv2 network as the backbone network and optimizes it for these problems.