Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM

Abstract

During the operation of the belt conveyor, foreign objects such as large gangue and anchor rods may be mixed into the conveyor belt, resulting in tears and fractures, which affect transportation efficiency and production safety. In this paper, we propose a lightweight target detection algorithm, GhostNet-CBAM-YOLOv4, to resolve the problem of the difficulty of detecting foreign objects at high-speed movement in an underground conveyor belt. The Kmeans++ clustering method was used to preprocess the data set to obtain the anchor box suitable for the foreign object size. The GhostNet lightweight module replaced the backbone network, reducing the model’s parameters. The CBAM attention module was introduced to enhance the ability of feature extraction facing the complex environment under the mine. The depth separable convolution was used to simplify the model structure and reduce the number of parameters and calculations. The detection accuracy of the improved method on the foreign body data set reached 99.32%, and the detection rate reached 54.7 FPS, which was 6.83% and 42.1% higher than the original YOLOv4 model, respectively. The improved method performed better than the original model on the other two datasets and could effectively avoid misdetection and omission detection. In comparison experiments with similar methods, our proposed method also demonstrated good performance, verifying its effectiveness.