Damage Detection of X-ray Image of Conveyor Belts with Steel Rope Cores Based on Improved FCOS Algorithm

• Published in: Shanghai jiao tong da xue xue bao. Yi xue ban Vol. 30; no. 2; p. 309
• Main Authors: Wang, Baomin, Ding, Hewei, Teng, Fei, Liu, Hongqin
• Format: Journal Article
• Language: English
• Published: Shanghai Shanghai Jiaotong University Press 01.04.2025
• Subjects: Algorithms; Artificial neural networks; Belt conveyors; Cores; Damage detection; Datasets; Flaw detection; Localization; Object recognition; Recall; Semantics; Steel
• ISSN: 1674-8115

Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image, a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection (FCOS) algorithm. The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm. The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure, which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks. Finally, the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer. In addition, the data enhancement methods such as rotating, mirroring, and scaling, were employed to enrich the image dataset so that the model is adequately trained. Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9% and 14.8% respectively, compared with the original algorithm. Meanwhile, compared with Fast R-CNN, Faster R-CNN, SSD, and YOLOv3, the improved FCOS algorithm has obvious advantages; detection precision rate and recall rate of the modified network reached 95.8% and 97.0% respectively. Furthermore, it demonstrated a higher detection accuracy without affecting the speed. The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.