## 4 Conclusions  
The purpose of this paper is to improve the traditional method of foreign body detection without an anchor frame, and to provide a high-stability and high-precision method for the foreign body detection of Molybdenum belt conveyors.  
Due to the problems of a too large recognition area, insufficient dark details and too much noise in the images collected on the Molybdenum ore belt conveyor, the collected images were processed with histogram equalization and median filter noise reduction, effectively reducing the impact of environmental factors on the later identification work. The introduction of atrous convolution through space improves the perception field of view of the Center-Net model. In order to solve the problem of unbalanced positive and negative samples in the training process of the metal ore belt conveyor, the parameters \(\alpha\_1\), \(\alpha\_2\), \(\beta\_1\), \(\beta\_2\) and \(\delta\) are introduced into the traditional loss function, to optimize the ratio of positive and negative samples in the training process, reduce the overall loss function value of the algorithm, and improve the detection accuracy of the algorithm. According to our experiment, it can be seen that the recognition accuracy of the method proposed in this paper for bar, polygon and circular objects reaches 0.822, 0.941 and 0.909, respectively, and the recognition accuracy of foreign objects on various conveyor belts is significantly improved. The high-precision identification of foreign objects on metal ore belt conveyors is realized. The experimental results show that the mine-Center-Net method proposed in this paper can effectively detect foreign objects on metal ore belt conveyors. Compared with the existing algorithms SSD, YOLO V3, Faster R-CNN and Center-Net, the recognition accuracy is significantly improved. The following work will focus on how to optimize the algorithm and improve the recognition speed of the algorithm.  
In the aforementioned work, we conducted research on the intelligent visual detection method for foreign objects on the molybdenum ore conveyor belt. By improving the existing framework network and optimizing the dataset, we achieved the optimization of the accuracy and timeliness of foreign object visual detection on the conveyor belt. However, there is still much room for improvement in the current work. For example, (1) enriching the types of foreign objects and detecting large ore blocks that may affect the stability of ore conveyor belt transportation; (2) innovating algorithms to enhance the stability of visual detection algorithms for possible dust conditions and visual motion blur that may be caused by high-speed conveyor belts, to improve their adaptability to industrial environments; (3) conducting further research on foreign object recognition to achieve a complete solution for the perception, monitoring, and operation control of foreign object recognition, localization, and exclusion. In the future, researchers can combine experiments with engineering projects to conduct follow-up research on the above-mentioned issues.  
Methodology, M.L.; software, M.L.; formal analysis, M.L.; investigation, M.L.; resources, C.L.; data curation, X.Z.; writing--original draft preparation, X.Y.; writing--review and editing, X.Y.; visualization, R.H.; supervision, M.L.; project administration, M.L.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of China (51974223, title: \"Research on Intelligent Fusion and situation assessment of multi-source heterogeneous flow data of rock failure in underground metal mines\"), and the Natural Science Foundation of Shaanxi Province (2019 JLP-16, title: \"Research and development of key technologies for intelligent production control and intelligent decision-making of open pit coal mine under cloud service\").  
Not applicable.  
Data Availability Statement: Data is contained within the article.