Abstract
This article designs a mobile robot applied to the inspection task of coke oven basement, and conducts in-depth analysis on the anomaly detection of the chain in the basement. Because of the absence of fault samples, the detection accuracy of normal state chains should be improved. When no detection target appears in the image, it is considered as an abnormal chain state. In addition, using environmental information to add features to the chain can improve the recognition accuracy of the normal working state of the chain. In order to avoid over-fitting of the model, light changes are used to enrich the content of the dataset, while reducing the dependence of the model on external added features. After experimental comparison, Mobilenet-v3-large is finally selected as the backbone network of YOLO-V3 algorithm. This model has been successfully deployed in the coke oven inspection robot detection system, which can detect normal working chains in complex environment. The accuracy rate reaches 99\(\%\).
National key research and development program (2020YFB1313300).
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Ma, Y., Chen, L., Ji, L., Zuo, B., Liu, B. (2023). Research on Chain Detection of Coke Oven Inspection Robot in Complex Environment. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14274. Springer, Singapore. https://doi.org/10.1007/978-981-99-6501-4_26
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DOI: https://doi.org/10.1007/978-981-99-6501-4_26
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