# Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM
## 1 Introduction  
China is the country with the highest energy consumption and the most significant coal consumption. As the primary energy source in China, coal has always dominated the direction of energy production and the consumption structure in China, ensuring the efficiency and safety of coal production and transportation for the stability of energy development. According to statistics, China's coal output reached 4.07 billion tons in 2021: an increase of 4.7% over the same period of last year. Coal consumption advanced by 5.24 billion tons, an increase of 5.2% over the same period of last year, and coal consumption accounted for 56.0% of the national energy consumption [1]. It can be seen that the coal industry not only plays an irreplaceable role in promoting national economic development but also plays an essential role in improving the quality of life and the living standards of people [2]. The belt conveyor is the leading equipment for underground coal mine transportation, which has the advantages of a large load, low economic cost, and long-distance transport and undertakes important tasks in coal transportation. The smooth operation of belt conveyors and their safe production are critical conditions for coal transportation. A conveyor belt is the core and weakest part of the belt conveyor, and its maintenance cost accounts for about 40% of the total cost. However, in the complex environment of coal development, some uncontrollable problems could bring potential safety hazards to the belt conveyor during transportation, such as cracks and jams in the transmission joints orthe tearing of the conveyor belt [3]. Due to the invasion of foreign objects (gangue, anchor, angle iron, logs), affects the production efficiency of coal mines and the transportation efficiency of belt conveyors, causing substantial economic losses to mine production. In severe cases, it may endanger the lives and safety of construction workers and cause serious coal mining accidents. Therefore, the real-time and timely detection and handling of non-coal foreign objects on the belt conveyor under the mine can minimize the damage caused by foreign objects to the conveyor and improve the utilization rate of coal, which is of great significance to the safe and stable production of coal mines and the improvement of production efficiency. The manual detection method and X-ray method [4] are the traditional methods for foreign body detection. According to research, small coal mines still use the manual sorting method of foreign materials, and workers work long hours in a dark and humid environment with low productivity, wasting a lot of human resources and making it difficult to protect their health. X-ray detection is based on the difference in energy absorption coefficients of elements in foreign and non-foreign objects. Applying the radiation method to foreign object detection is expensive and can be hazardous to workers' health. In recent years, with the rapid development of machine vision technology, image recognition technology has been applied to foreign object detection, replacing the traditional foreign object detection methods. Image recognition techniques use grayscale information features [5; 6; 7] to analyze texture information and support vector machines to recognize it. All these methods require human-specified features or complex logical inference operations and are subject to the influence of the external environment, with poor generalization and robustness. With the widespread use of convolutional neural networks and improved computer hardware capabilities, object detection algorithms based on deep learning have made significant progress. The current target detection algorithms can be divided into two categories. One is the two-stage target detection algorithm; the representative algorithm is Faster-RCNN. The detection principle is based on target detection by extracting candidate regions in the image and then classifying and regressing each candidate region.