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

Abstract

This paper proposes a cascade deep convolutional neural network to address the loosening 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 2 -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.

FIQ: A Fastener Inspection and Quantization Method Based on Mask FRCN

“…Wei et al [5] used Faster-RCNN and YOLO V3 for railway area detection and fastener state identification. Wang [6] proposed a multi-size input training method, and Ling et al [7] introduced a hierarchical features-based model. Liu et al [8] and Zheng et al [9] proposed deep learning approaches that improved performance.…”

Bolt Loosening Detection Using Key-Point Detection Enhanced by Synthetic Datasets

“…Among them, Pham et al [23] used Solidworks to model bolts for expanding the dataset. Some research determines bolt loosening by making manual marks on the bolt and nut and identifying the relationship between the bolt and the marks by a target detection algorithm [25][26][27][28][29]. Some research qualitatively judges whether a bolt is loose by the length of the bolt extension [30,31].…”