YOLO-STOD: an industrial conveyor belt tear detection model based on Yolov5 algorithm

Real-time detection of conveyor belt tearing is of great significance to ensure mining in the coal industry. The longitudinal tear damage problem of conveyor belts has the characteristics of multi-scale, abundant small targets, and complex interference sources. Therefore, in order to improve the performance of small-size tear damage detection algorithms under complex interference, a visual detection method YOLO-STOD based on deep learning was proposed. Firstly, a multi-case conveyor belt tear dataset is developed for complex interference and small-size detection. Second, the detection method YOLO-STOD is designed, which utilizes the BotNet attention mechanism to extract multi-dimensional tearing features, enhancing the model's feature extraction ability for small targets and enables the model to converge quickly under the conditions of few samples. Secondly, Shape_IOU is utilized to calculate the training loss, and the shape regression loss of the bounding box itself is considered to enhance the robustness of the model. The experimental results fully proved the effectiveness of the YOLO-STOD detection method, which constantly surpasses the competing methods and achieves 91.2%, 91.9%, and 190.966 detection accuracy and detection speed in terms of recall, Map value, and FPS, respectively, which is able to satisfy the needs of industrial real-time detection and is expected to be used in the real-time detection of conveyor belt tearing in the industrial field.