Infrared defect detection for battery pole piece using continuous laser-line scanning and entropy-based fusion

To address the challenge of efficiently detecting surface defects in power battery pole piece electrodes during production, this paper proposes an infrared image processing method based on continuous laser line scanning thermography (CLST). This method integrates multi-column entropy weight fusion and saliency detection methods to enhance the visibility and detection accuracy of micro-defects in infrared images. First, a frame selection resampling strategy is designed to correct the nonlinear geometric distortion in thermal images caused by the mismatch between the specimen’s moving speed and the infrared camera’s sampling frequency. Second, by extracting multi-column data from the images and performing entropy weight fusion (EWF), the contrast and texture details of local thermal anomaly regions are enhanced. Finally, a saliency enhancement (SE) detection algorithm is introduced to highlight defect regions, improving the separation capability and recognition robustness for micro-defects compared to traditional threshold segmentation methods. The experimental results show that this method can stably detect 0.1 mm scratches, pinholes and shadow marks at a scanning speed of 0.8–1.6 mm/s. For dark spot defects with weak thermal response, the SE method needs to be used for processing.