Multi-feature fusion weld crack segmentation method based on visible image detection and ECPT

Due to the wide application of welding in the modern industry, effective detection of weld surface defects is an important measure to ensure the quality of components, monitor the Service life of the structure, and ensure the safety of users. However, there are wrinkles and stains on the weld surface, which makes detection difficult. Based on the dynamic detection of pulsed eddy current thermography, a multi-feature fusion algorithm of infrared features and visible information is proposed in this paper. In dynamic detection, the relative position of cracks in the field of view is constantly changing, therefore, the thermal image sequences are spatially aligned to obtain the transient thermal response curve in static mode. Feature extraction and dimensionality reduction of thermal image sequences are carried out in time domain. The processed data is fused with the visible image features, and classified in pixel-level applying the pattern recognition network. The experimental results show that the proposed algorithm can effectively suppress the noise caused by weld texture and surface stains, and obtain more clear and accurate defect information. All 21 weld surface defects can be detected, and the detection ability is greatly improved.