A Real-Time Detection Algorithm for High-Precision Conveyor Belt Deviation Applied to Edge Computing Devices

Conveyor belt deviation is one of the common factors leading to belt conveyor failure. Real-time detection of conveyor belt deviation is crucial for realizing intelligent detection of the belt conveyor. This paper proposes a Lightweight and Efficient Conveyor Belt Deviation Detection algorithm based on YOLOv7 for edge computing devices. This algorithm incorporates the Mixed Local Channel Attention (MLCA), ELAN-Depthwise Separable Convolution (ELAN-DW), and SPP-Lightweight (SPPL) modules, along with a dynamic decoupled head (D-Dhead) that utilizes both channel and spatial attention mechanisms. According to the self-built dataset, the model’s test accuracy is 0.936. The results show that the algorithm has strong feature extraction capabilities, high real-time performance, and high detection accuracy in scenarios with blurring and insufficient lighting. The algorithm proposed in this paper provides a reference for research on intelligent detection technology for conveyor belt deviation and offers a new solution for edge intelligent fault diagnosis method of belt conveyor, which has important engineering application value.