Automatic Weld Defect Detection in Steel Plates Based on Ultrasonic Total Focusing Method and Multi-layer Feature Fusion Target Detection Network

doi:10.3901/JME.2025.04.055

Abstract

Abstract: Defects in metal welding structures can cause serious harm to key equipment during service. Traditional defect identification is mainly based on manual inspection. However, the amount of actual inspection data is huge, and defects vary in shape and size, making the inspection results susceptible to various influencing factors. A multi-layer feature fusion target detection network is proposed to intelligently identify the ultrasonic total-focusing image of steel plate welds. Since the proportion of defective pixels in ultrasound images is low and there are many noises and artifacts, the original VGG backbone network is replaced with a dense convolutional network with feature reuse and fewer parameters to optimize the computing resources and speed of the model. At the same time, a multi-layer network fusion module is embedded to fuse the upper and lower adjacent network feature maps, fully combining large-size features and small-size features to improve the detection ability of the model. In addition, a spatial attention mechanism is embedded to enhance defect information and suppress background noise. Finally, the intersection over union ratio non-maximum suppression algorithm in the original SSD is replaced with a distance intersection over union ratio to better detect closely spaced defects in using simulation and experiments to obtain the ultrasonic total-focusing data set of typical weld defects for model testing, the results show that the average accuracy of the model is 10.45% higher than the original SSD model, and the detection frame rate is increased by 17%. The proposed multi-layer feature fusion target detection network can be used for fast, accurate and automatic identification of large-volume ultrasonic total-focusing inspection results of welded steel structures.

Key words: total focusing method, deep learning, weld defect, feature fusion

CLC Number:

TG156

QIN Feihong, YUAN Maodan, ZHU Duanyang, LIU Xiaorui, LI Ming, JI Xuanrong. Automatic Weld Defect Detection in Steel Plates Based on Ultrasonic Total Focusing Method and Multi-layer Feature Fusion Target Detection Network[J]. Journal of Mechanical Engineering, 2025, 61(4): 55-66.

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