The invention belongs to the technical field of underground coal mine conveying belt detection, and discloses a method for detecting a foreign matter target of a underground coal mine conveying belt, system, equipment and a terminal, and the method comprises the steps: making a training sample and a test sample through an underground coal mine conveying belt monitoring video; intercepting and labeling foreign matters from the obtained video by using a Labelmg labeling tool, and carrying out adaptive histogram equalization on the conveyor belt image; under a YOLOv5 algorithm framework, introducing an attention model CBAM, simplifying network parameters by using depth separable convolution, optimizing a loss function, and constructing a detection model. According to the method, the foreign matters on the conveying belt can be detected under the conditions of coal dust interference, non-uniform illumination and high-speed movement of the conveying belt, so that higher detection precision is achieved, and the real-time requirement can be well met. Experimental results show that the accuracy and the recall rate of the foreign matter target detection algorithm for the underground coal mine conveying belt are the highest, and meanwhile the algorithm keeps good real-time performance.