## 1 Introduction
Faster R-CNN is a typical two-stage object detection algorithm that uses RPN to extract candidate regions and classifies and locates them through RoI Pooling and fully connected layers. On the other hand, one-stage object detection algorithms are more concise. They transform the object detection problem into a regression problem and directly output the object's bounding box coordinates and class probabilities through a convolutional neural network. Generally, two-stage algorithms are slower but more accurate, while one-stage algorithms are faster but slightly less accurate. However, with network design and improvements, the accuracy of one-stage algorithms can be effectively enhanced, making the overall model logic simpler with better development prospects. Therefore, we choose the single-stage object detection algorithm as the foundation of our research. The developed machine vision foreign single-stage object detection modes can be divided into two types: anchor frame type and anchorless frame type. The famous YOLO series algorithm adopts the anchor box mode, which predefines a set of fixed bounding boxes (anchor boxes) with different sizes and aspect ratios to adapt to targets of different sizes and shapes. The algorithm detects targets by adjusting these predefined anchor boxes. This method performs well when dealing with a large number of standard targets but may not perform as well on unconventional or small targets. Anchor-free methods such as Center-Net do not predefine fixed bounding boxes but directly predict the edges or center points of the targets. This method is more flexible when handling irregular shapes or small targets. In terms of the proportion of foreign objects to the main body of the conveyor belt, the anchor-free method shows more potential in terms of detection accuracy. For example, SSD [6; 7; 8], YOLO V3 [9; 10; 11], and Faster R-CNN [12] have anchor frames. The anchor frame method often leads to a decrease in model accuracy [13; 14] due to the processing of the non-maximum suppression value (NMS). Center-Net [15; 16; 17] is an anchor-free method, and the anchor-free method is limited by the receptive field of view of the pooling layer feature extraction network, and a large number of foreign object details will be lost. At the same time, in the anchor-free method, the detection target predicts the point corresponding to the center position through the heat map, which will lead to the extreme imbalance of the foreign object sample and the problem that the loss function is difficult to converge. Based on the research mentioned above, we have focused on improving the accuracy of anchor-free object detection [18] on the molybdenum ore conveyor belt. Firstly, we enhanced the algorithm to improve the model's ability to capture multi-scale features in complex backgrounds. Based on the anchor-free object detection method Center-Net, we introduced the dilation rate parameter into atrous convolution [19] to reduce the loss of detailed foreign object information when passing through the pooling layer. By adjusting parameters with positive and negative samples, we reduced the overall loss function value and improved the accuracy of foreign object detection [20]. In order to address the existing gaps in research, we propose an enhanced deep learning network for object detection specifically designed for identifying foreign objects on molybdenum mine conveyor belts, building upon the Center-Net framework to achieve improved detection capabilities for small targets in complex backgrounds. First, in the Method section of this paper, we systematically outline the detection process for foreign objects on conveyor belts and discuss the current state of anchor-free object detection networks. We introduce a novel convolutional pattern and redesign the loss function, enabling the model to achieve greater accuracy in detecting small targets against complex backgrounds.