@Article{cmes.2022.022143,
AUTHOR = {Ji Wang, Liming Li, Shubin Zheng, Shuguang Zhao, Xiaodong Chai, Lele Peng, Weiwei Qi, Qianqian Tong},
TITLE = {A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {134},
YEAR = {2023},
NUMBER = {3},
PAGES = {1671--1706},
URL = {http://www.techscience.com/CMES/v134n3/49753},
ISSN = {1526-1506},
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 A<sup>2</sup>-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.},
DOI = {10.32604/cmes.2022.022143}
}