YOLO-Xweld: Efficiently Detecting Pipeline Welding Defects in X-Ray Images for Constrained Environments
Jun Yang, Bo Fu, Jinquan Zeng, Shengxi Wu
Abstract
X-ray inspection of pipeline welds brings benefits for the industrial quality and safety of production. However, noise and complex texture structure in X-ray images caused by issues such as poor contrast and blurry defects lead to extraordinary obstacles for lightweight and high-precision identification to satisfy almost 100% accuracy requirements, especially in constrained environments. This paper proposes a novel method, named YOLO-Xweld, to detect pipeline welding defects for constrained application scenarios. YOLO-Xweld is derived from YOLO v3-tiny by adding an SPP module to the backbone network to enhance multi-scale feature extraction, and weakening large-scale network detection branch based on characteristics of pipeline weld X-ray images. Depthwise separable convolution is introduced to achieve a lightweight model and reduce the dependence on the high-performance hardware. Experiments show that the number of the model's parameters and floating-point operations have dropped by over 70%, and the model size has been reduced to 1/15 of the original size, while the test accuracy is close to 100%. This is of great significance in reducing the dependence on high-performance hardware and enhancing the convenience and operability during inspections in the constrained pipeline weld detection environment.