A real-time and accurate detection approach for bucket teeth falling off based on improved YOLOX

Abstract

Abstract. An electric shovel is a bucket-equipped mining excavator widely used in open-pit mining today. The prolonged direct impact between the bucket teeth and the ore during the mining process will cause the teeth to loosen prematurely or even break, resulting in unplanned downtime and productivity losses. To solve this problem, we propose a real-time and accurate detection algorithm of bucket teeth falling off based on improved YOLOX. Firstly, to solve the problem of poor detection effect caused by uneven illumination, the dilated convolution attention mechanism is added to enhance the feature expression ability of the target in complex backgrounds so as to improve the detection accuracy of the target. Secondly, considering the high computing cost and large delay of the embedded device, the deep separable convolution is used to replace the traditional convolution in the feature pyramid network, and the model compression strategy is used to prune the redundant channels in the network, reduce the model volume, and improve the detection speed. The performance test is carried out on the self-constructed dataset of WK-10 electric shovel. The experimental results show that, compared with the YOLOX model, the mean average precision of the algorithm in this paper reaches 95.26 %, only 0.33 % lower, while the detection speed is 50.8 fps, 11.9 fps higher, and the model volume is 28.42 MB, which is reduced to 29.46 % of the original. Compared with many other existing methods, the target detection algorithm proposed in this paper has the advantages of higher precision, smaller model volume, and faster speed. It can meet the requirements of real-time and accurate detection of the bucket teeth falling off.