Abstract
Due to the slow detection speed, low accuracy, and small detection range of existing methods for detecting belt deviation in belt conveyors, this paper introduces an enhanced Ultra-Fast Lane Detection (UFLD) algorithm that leverages deep learning for the detection of belt deviation. Based on the UFLD algorithm, a variable step-size row anchor division method is proposed, and the Convolutional Block Attention Module (CBAM) is added to the network to enhance the feature extraction capabilities. Furthermore, improvements are made to the convolution operations in the ResNet-18 Stem and the downsampling operations in the residual modules, thereby enhancing the network's ability to detect the edges of conveyor belts. Based on the established experimental platform, a high-definition camera equipped with a track-type inspection robot was used to inspect the entire belt conveyor, covering the whole of the transmission line. The conveyor belt operation datasets collected under various working conditions were used to train and comparatively study the DHT, YOLOv5, LaneNet, SAD, and UFLD algorithms. The experimental outcomes demonstrate that the algorithm introduced in this article outperforms the other algorithms, achieving an F1-measure of 90.41%, an accuracy rate of 94.27%, and a detection speed of 39 frames per second (FPS), meeting the real-time diagnostic needs for belt misalignment in the coal mining industry.