Open Access

ARTICLE

A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network

Ji Wang¹, Liming Li^1,2,3, Shubin Zheng^1,3, Shuguang Zhao², Xiaodong Chai^1,3, Lele Peng^1,3, Weiwei Qi^1,3, Qianqian Tong¹

1 School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai, 201620, China
2 School of Information Science and Technology, Donghua University, Shanghai, 201620, China
3 Shanghai Engineering Research Centre of Vibration and Noise Control Technologies for Rail Transit, Shanghai University of Engineering Science, Shanghai, 201620, China

* Corresponding Author: Liming Li. Email:

Computer Modeling in Engineering & Sciences 2023, 134(3), 1671-1706. https://doi.org/10.32604/cmes.2022.022143

Received 02 February 2022; Accepted 11 May 2022; Issue published 20 September 2022

Abstract

Loosening detection; cascade deep convolutional neural network; object localization; saliency detection problem of bolts on axlebox covers. Firstly, an SSD network based on ResNet50 and CBAM module by improving bolt image features is proposed for locating bolts on axlebox covers. And then, the A²-PFN is proposed according to the slender features of the marker lines for extracting more accurate marker lines regions of the bolts. Finally, a rectangular approximation method is proposed to regularize the marker line regions as a way to calculate the angle of the marker line and plot all the angle values into an angle table, according to which the criteria of the angle table can determine whether the bolt with the marker line is in danger of loosening. Meanwhile, our improved algorithm is compared with the pre-improved algorithm in the object localization stage. The results show that our proposed method has a significant improvement in both detection accuracy and detection speed, where our mAP (IoU = 0.75) reaches 0.77 and fps reaches 16.6. And in the saliency detection stage, after qualitative comparison and quantitative comparison, our method significantly outperforms other state-of-the-art methods, where our MAE reaches 0.092, F-measure reaches 0.948 and AUC reaches 0.943. Ultimately, according to the angle table, out of 676 bolt samples, a total of 60 bolts are loose, 69 bolts are at risk of loosening, and 547 bolts are tightened.

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Graphical Abstract

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