Deep learning-based damage detection of mining conveyor belt

Application of Lightweight Convolutional Neural Network for Damage Detection of Conveyor Belt

“…When compared with the results obtained by Efficientnet-Yolov3 in Ref. [3], the lightweight network proposed in this paper has a great advantage in detection speed, except for MobilenetV2-Yolov4-1.3, and the fastest detection speed achieved in this paper is approximately 1.7 times faster than the fastest detection speed achiveved by Efficientnet-Yolov3, reaching 70 FPS. However, due to the compression and adjustment of channel number, the improved algorithm proposed in this paper is relatively deficient in image information feature extraction ability, and does not achieve a higher detection accuracy as mentioned in Ref.…”

“…It can be seen that the Resnet50 based Centernet algorithm has reached the highest average prediction accuracy of 95.05% with a fastest detection speed of 32.4 FPS; The second part is the detection results obtained in Ref. [3]. Among them, the EfficientNetB0 based EfficientNet-B0-Yolov3 has achieved the fastest detection speed of 41.91 FPS, and the EfficientNet-B4-Yolov3 has achieved the highest detection accuracy with 97.26%.…”

“…The conveyor belt damage dataset used in this paper is provided in Ref. [3], which contains 3000 images. The damage types are divided into four categories: surface wear, surface damage, tear and breakdown, and each type of damage occupies 1/4.…”

“…At present, it is developing towards long-distance, high-speed, smallradius space turning, and intelligence [1]. The intelligentization of belt conveyors refers to the realization of self-perception and status adjustment of its operating status through modern sensing technology and artificial intelligence, while the realization of autonomous operation and unattended operation of the equipment [2,3].…”

“…Then a deep learning-based detection method was mentioned in Ref. [3]: by constructing a conveyor belt damage dataset and classifying the belt damage types into four categories, namely surface wear, surface damage, breakdown, and tear, an Efficientnet-Yolov3-based target detection network was proposed to classify and locate the damage, achieved the highest prediction accuracy of 97.26% and the fastest prediction speed effect of 42 FPS on the dataset.…”