The work aims to propose a high-robustness and high-efficiency packaging defect processing method to address the challenges of segmentation blur and detection errors in traditional methods when dealing with low-contrast, highly interfered, and subtle defect images of flexible printed circuit (FPC) ribbon cables, and to enhance the accuracy and efficiency of FPC defect image segmentation and detection. An image segmentation and defect detection method was constructed based on a Parallel Dynamic Role Memory Grey Wolf Optimization (PDM-GWO) algorithm. The optimization capabilities were improved through dynamic role assignment and historical position memory, while introducing a master-slave parallel architecture to enhance computational efficiency. In the segmentation phase, a PDM-GWO optimized multi-threshold strategy was adopted to extract clear edges. In the detection stage, the coordinates of the wiring were obtained based on edge detection, the geometric features were extracted by integrating RANSAC fitting, and the Z-score statistical analysis was combined to realize the recognition of multiple types of defects. Extensive image experiments demonstrated that the proposed method achieved average values of 22.42 dB for PSNR, 0.964 for SSIM, and 0.933 for IoU, all surpassing standard GWO and typical improved algorithms. For defect detection, the average mean average precision reached 0.990 6, with a processing speed of 9.63 frames per second, outperforming mainstream methods such as YOLOv9 and Faster-RCNN. The proposed method exhibits significant advantages in image segmentation quality, detection accuracy, and operational efficiency. It is well-suited for micro-defect detection in complex industrial conditions on automated packaging lines, demonstrating excellent engineering practicality and promising generalization value.
Key words
flexible printed circuit /
packaging defect detection /
image segmentation /
grey wolf optimization algorithm /
dynamic role /
historical memory /
parallel computing
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References
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