Rapid Detection of Tools of Railway Works in the Full Time Domain

Zhaohui Zheng, Yuncheng Luo (), Shaoyi Li, Zhaoyong Fan, Xi Li, Jianping Ju, Mingyu Lin and Zijian Wang
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Zhaohui Zheng: School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang 330108, China
Yuncheng Luo: School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang 330108, China
Shaoyi Li: School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang 330108, China
Zhaoyong Fan: School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang 330108, China
Xi Li: School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang 330108, China
Jianping Ju: School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang 330108, China
Mingyu Lin: School of Artificial Intelligence, Hubei Business College, Wuhan 430070, China
Zijian Wang: School of Information and Artificial Intelligence, Nanchang Institute of Science & Technology, Nanchang 330108, China

Sustainability, 2022, vol. 14, issue 20, 1-13

Abstract: Construction tool detection is an important link in the operation and maintenance management of professional facilities in public works. Due to the large number and types of construction equipment and the complex and changeable construction environment, manual checking and inventory are still required. It is very challenging to count the variety of tools in a full-time environment automatically. To solve this problem, this paper aims to develop a full-time domain target detection system based on a deep learning network for difficult, complex railway environment image recognition. First, for the different time domain images, the image enhancement network with brightness channel decision is used to set different processing weights according to the images in different time domains to ensure the robustness of image enhancement in the entire time domain. Then, in view of the collected complex environment and the overlapping placement of the construction tools, a lightweight attention module is added on the basis of YOLOX, which makes the detection more purposeful, and the features cover more parts of the object to be recognized to improve the model. Overall detection performance. At the same time, the CIOU loss function is used to consider the distance fully, overlap rate, and penalty between the two detection frames, which is reflected in the final detection results, which can bring more stable target frame regression and further improve the recognition accuracy of the model. Experiments on the railway engineering dataset show that our RYOLO achieves a mAP of 77.26% for multiple tools and a count frame rate of 32.25FPS. Compared with YOLOX, mAP increased by 3.16%, especially the AP of woven bags with a high overlap rate increased from 0.15 to 0.57. Therefore, the target detection system proposed in this paper has better environmental adaptability and higher detection accuracy in complex railway environments, which is of great significance to the development of railway engineering intelligence.

Keywords: railway instrument detection; deep learning; object detection; luminance enhancement (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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