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

Related subject area

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

Akter, A. and Basak, H.: Design and analysis of biomimetics based excavator bucket and tooth, P. I. Mech. Eng. E-J. Pro, 236, 1167–1175, https://doi.org/10.1177/09544089211057645, 2022. 
Alma'aitah, A. and Hassanein, H.: Utilizing Sprouts WSN platform for equipment detection and localization in harsh environments, in: 39th Annual IEEE Conference on Local Computer Networks Workshops, 8–11 September 2014, Edmonton, Canada, 777–783, https://doi.org/10.1109/lcnw.2014.6927734, 2014. 
Bai, D., Sun, Y., Tao, B., Tong, X., Xu, M., Jiang, G., Chen, B., Cao, Y., Sun, N., and Li, Z.: Improved single shot multibox detector target detection method based on deep feature fusion, Concurr. Comp.-Pract. E., 34, e6614, https://doi.org/10.1002/CPE.6614, 2022. 
Bello, R., Mohamed, A., and Talib, A.: Enhanced Mask R-CNN for herd segmentation, Int. J. Agr. Biol. Eng., 14, 238–244, https://doi.org/10.25165/j.ijabe.20211404.6398, 2021. 
Buiu, C., Danaila, V., and Raduta, C.: MobileNetV2 ensemble for cervical precancerous lesions classification, Processes, 8, 595, https://doi.org/10.3390/pr8050595, 2020. 
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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.