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Article type: Research Article
Authors: Luo, Jiangnana | Cai, Jinyub | Li, Jianpinga; * | Gao, Jiuhuac | Zhou, Feng | Chen, Kailanga | Liu, Leia | Hao, Mengdaa
Affiliations: [a] School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, China | [b] School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China | [c] China Coal Research Institute, Beijing, China | [d] College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao, China
Correspondence: [*] Corresponding author. Jianping Li, School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China. E-mail: jdljping@cumt.edu.cn.
Abstract: During the process of gas hole drilling, automatic loading and unloading drilling rod by robotic arm ensures the safety of personnel and drilling efficiency. Accurate recognition of drilling rod target is a prerequisite for precise positioning. However, the presence of dark and dust underground coal mines presents the great challenge in detecting and recognizing drilling rods during the automatic drill loading and uploading process. To solve this problem, We have designed a drilling rod target detection and segmentation technology based on generating adversarial network(GAN). Furthermore, we carried out experiments to compare the recognition performance of drilling rods of different colors, including black, blue, and yellow, in the dark and dusty environment. The results indicate that the drilling rod recognition method proposed in this paper demonstrates high accuracy and robustness even in dark and dusty environment, better than other commonly used segmentation networks. Notably, the recognition accuracy of yellow drilling rods surpasses that of blue and black drilling rods.
Keywords: Dark and dusty environment, drilling rod recognition segmentation, deep learning, GAN, spatial attention mechanism
DOI: 10.3233/JIFS-232162
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5481-5492, 2023
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