Articles | Volume 13, issue 2
https://doi.org/10.5194/ms-13-979-2022
https://doi.org/10.5194/ms-13-979-2022
Research article
 | 
25 Nov 2022
Research article |  | 25 Nov 2022

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

Jinnan Lu and Yang Liu

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Short summary
Aiming at the potential safety problems caused by the looseness of bucket teeth of the electric shovel, we propose an improved YOLOX algorithm. After introducing the dilated CBAM, the deep separable convolution, and model compression, the mAP is decreased by 0.33 %, the detection speed is increased by 11.9 fps, and the model volume is reduced to 29.46 % of the YOLOX model. It provides a theoretical basis and technical support for the development of intelligent mines and mining intelligence.