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

Abstract

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.

摘要

针对钢丝绳芯输送带损伤X射线图像识别中识别目标长、窄几何特征和泛化能力较差等问题, 提出一种基于改进FCOS算法的钢丝绳芯输送带损伤X射线图像识别方法。在改进算法中采用CIoU损失函数优化包围框回归性能;在特征融合网络结构中添加自适应融合路径, 充分利用多尺度特征融合网络定位精准、语义性**的特点, 提出新的特征融合网络结构。最后, 利用某探伤装置公司提供的钢丝绳芯输送带损伤X射线图像数据集进行网络训练和验证, 并采用镜像翻转、旋转以及缩放等数据增**方法丰富钢丝绳芯输送带损伤特征图像数据集, 以保证模型得以充分训练。实验结果表明, 改进算法在原算法基础上查准率提高20.9%, 查全率提高14.8%。同时与Fast R-CNN、Faster R-CNN、SSD、YOLOv3网络进行比较, 优势较为明显, 查准率和查全率分别达到95.8%、97.0%, 且在检测速度不影响的情况下, 有更高的检测精度。该研究结果对钢丝绳芯输送带损伤的自动识别和检测有重要参考意义。