Automotive adhesive defect detection based on improved YOLOv8

In automotive adhesive defect detection, manual inspection suffers from low efficiency and blind spots in human vision, which affects the performance of parts. Therefore, automated detection methods are particularly important. To address the issue of adhesive defects significantly impacting production during automated gluing processes, we propose an adhesive defect detection method for automotive applications based on the improved YOLOv8 (named YOLOv8n-SSE). First, we used the SSE (skip squeeze and excitation) attention mechanism in the backbone part to dynamically adjust the importance of different channels in our model and allow our model to selectively focus on important features. Then, the original bounding box loss function is replaced by the WIoU loss function. Experimental results demonstrate that this method improves the mAP50 of the original YOLOv8n by 3.25% and achieves an average detection speed of 7.9ms per image, equivalent to 126.58 frames per second (FPS), meeting the real-time defect detection requirements.