## 2 Methods of Research
### Improvements of the Approach  
The target detection method with anchor boxes may cause a large number of anchor boxes to be removed by NMS [21]. In order to avoid this phenomenon, the network detection efficiency decreases. Using the anchor-free target detection method, the image recognition is realized by predicting the center point of the target in the detection map and returning to the location of the target. The algorithm greatly improves the detection efficiency of the network. Coner-Net, Coner-Net Lite, Center-Net, FCOS (Fully convolutional one-stage object detection) are longer target detection networks using anchor-free boxes [22]. 1. Center-Net Object Detection Algorithm  
The Center-Net model is divided into two steps. First, the model needs to predict the center point of the target detection object through the heat map, and then predict the recognition frame of the target object through the generated center point. Figure 2 shows the structure of Center-Net. The image is sent to the backbone network for feature extraction, and then input to the key point generation module and the target box generation module, respectively. In the key point module, the heat map is used as the basic information to predict the center point, the center point offset and the target size, respectively, and, finally, the key points of the predicted object are obtained. In the target box generation module, the target box is  
Figure 1: Basic procedures for foreign body detection. Figure 2: Center-Net structure. generated by matching the corners belonging to the same pair according to the heat map. This method does not need to use NMS, which greatly improves the detection efficiency of the target detection model. 2. Hourglass-104 Network Architecture  
In this paper, the Hourglass-104 network structure is used to extract the foreign matter characteristics of the metal mine belt conveyor. The Hourglass-104 network structure is composed of two modules similar to the Hourglass structure A series of convolutional layers, where residual unit modules, feature fusion modules and upsampling layers form a Hourglass module [23; 24]. A single Hourglass structural module is shown in Figure 3.  
The specific working steps of Hourglass-104 are as follows: 1 Input the original image to the preprocessing module of Hourglass-104 to reduce the resolution of the original image by 34. 2 Input the residual unit, and perform 5 downsampling processes through convolution with a step size of 2. The downsampling process increases the number of channels of the feature map. 3 Upsampling is performed five times to bring the image resolution back to the pre-processed size, while also reducing the number of feature channels. 3. Expansion of the receptive field  
Since the characteristics of deep learning often require a lot of computation, traditional convolutional neural networks reduce the size of the input image through the pooling layer, thereby reducing the time required for computation. In order to avoid the loss of foreign body detail information caused by the pooling layer, a dilation rate parameter is introduced, which is added to the original convolution to make the original convolution into an atrous convolution. Taking a 3 \(\times\) 3 convolution as an example, Figure 4 below shows the difference between the ordinary convolution and the atrous convolution. The three large boxes in Figure 5, named a, b, and c represent the input images, respectively; the black dots represent the 3 \(\times\) 3 convolution, and the shaded gray area in the figure is the recognition area of the convolution kernel. a is an ordinary 3 \(\times\) 3 convolution, and the receptive field of view is 3 \(\times\) 3. Picture b shows atrous convolution with an expansion rate of 2, and the receptive field of view is increased to 5 \(\times\) 5. Picture c  
Figure 4: Atrous convolution contrast. Figure 3: Hourglass structure module. shows atrous convolution with an expansion rate of 3, and the perceived field of view is increased to \(8\times 8\).