Articles | Volume 13, issue 2
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

Related subject area

Subject: Mechanisms and Robotics | Techniques and Approaches: Mathematical Modeling and Analysis
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Cited articles

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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.