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A weld is the main connection form of special equipment, and a weld is also the most vulnerable part of special equipment. Therefore, an effective detection of a weld is of great significance to improve the safety of special equipment. The traditional inspection method is not only time-consuming and labor-intensive, but also expensive. The welding seam tracking and inspection robot can greatly improve the inspection efficiency and save on inspection costs. Therefore, this paper proposes a welding seam tracking and inspection robot based on YOLOv8s-seg. Firstly, the MobileNetV3 lightweight backbone network is used to replace the backbone part of YOLOv8s-seg to reduce the model parameters. Secondly, we reconstruct C2f and prune the number of output channels of the new building module C2fGhost. Finally, in order to make up for the precision loss caused by the lightweight model, we add an EMA attention mechanism after each detection layer in the neck part of the model. The experimental results show that the accuracy of weld recognition reaches 97.8%, and the model size is only 4.88 MB. The improved model is embedded in Jetson nano, a robot control system for seam tracking and detection, and TensorRT is used to accelerate the reasoning of the model. The total reasoning time from image segmentation to path fitting is only 54 ms, which meets the real-time requirements of the robot for seam tracking and detection, and realizes the path planning of the robot for inspecting the seam efficiently and accurately.
Zhao et al. (Fri,) studied this question.