## 1 Introduction  
Molybdenum is an important additive in the steel industry, primarily used to enhance the strength, toughness, corrosion resistance, and high-temperature performance of steel. It is widely used to produce various alloys. China is one of the major production areas for molybdenum. Most molybdenum is found in the form of molybdenum within granite mountains. In open-pit mining, the extraction primarily involves blasting, shoveling, and belt transportation, with a particular reliance on long-distance conveyor belts. Foreign objects (drill roots, shovel teeth, and I-beams) on metal mining belt conveyors are one of the main causes of belt damage, tearing and grinder wear, seriously threatening the safe and stable operation of mine production. Therefore, metal mining enterprises urgently need to accurately detect foreign objects on the conveyor belt, remove foreign objects in advance or give a timely warning, and prohibit pre-incident accidents, so as to reduce the economic loss and the impact of safety accidents caused by foreign objects. The current fault protection system of belt conveyors can only perform self-checks on conveyor operation faults, usually during the shutdown after the accident has occurred. The traditional conveyor belt foreign object detection technology mainly includes radar detection, a metal detector and manual detection. The use of the above foreign object detection methods has disadvantages such as high cost, few detection types, and difficulty regarding equipment installation, operation and maintenance, and it is difficult to widely popularize in the actual production of metal mining enterprises. In recent years, with the blowout development of artificial intelligence, the application of machine vision in various fields has become more and more mature, making machine vision possible as a simple, low-cost and reliable foreign object detection method for belt conveyors. Researchers initially use computer graphics methods to process the collected digital images of conveyor belts, using feature extraction operators to identify simple, unobstructed foreign objects. These methods could be applied to simple environments, such as foreign object detection on product production line conveyors, with the core idea being to detect objects, exclude non-foreign objects, and raise an alarm for foreign objects. Reverse inspection methods could detect unknown-shaped foreign objects, but they were only suitable for standardized production lines. For non-standardized conveyor belts such as coal and metal ores, reverse inspection was not feasible, and detection had to rely on pre-set common foreign objects. However, common foreign objects come in various shapes and can be obstructed. In the early days, scholars used feature extraction combined with machine learning methods to expand the detection capabilities of algorithms. They first used operators to extract local features and then employed machine learning classifiers (such as SVM [1]) for further classification [2]. With the increase in computing power, image deep learning [3] technology has been widely developed. Convolutional neural networks can provide machines with excellent image understanding capabilities, leading to the development of various image recognition and object detection methods based on this technology. For the problem of foreign object detection on conveyor belts, using object detection methods is a good choice. Object detection [4] is a supervised learning method where, after multiple rounds of annotated data learning, the algorithm can identify different shapes of objects, their sizes, and positions in the image matrix, and provide an estimate of confidence in their classification. Object detection algorithms have evolved into various approaches, mainly categorized into one-stage and two-stage modes. Two-stage object detection methods typically consist of two stages: first, a Region Proposal Network (RPN [5]) generates candidate regions, and then these regions are classified and precisely located.