| Summary: | Bubble defects in chip packaging can have an impact on the stability and reliability of the chip. Existing defect detection methods exhibit limited performance in identifying small-sized bubble defects and are highly susceptible to low contrast and noise in chip X-ray images, leading to missed and false detections. To address these challenges, we propose YOLO-DFA, a defect detection model based on improved YOLOv8 framework, to improve the defect detection accuracy. First, a Dual-Former attention mechanism is introduced to improve local and global feature integration, addressing missed detections of small bubble defects and weakening meaningless noise information. Second, a C2-CS module replaces the C2f module in YOLOv8, reducing spatial feature redundancy and computational complexity. Third, an improved Neck network incorporates a 3D-CBS module into the PAFPN network, enhancing the recognition of low contrast targets by strengthening multi-scale feature fusion. DySample is used for upsampling to minimize feature detail loss. Experimental results on the CXray dataset demonstrate that the YOLO-DFA model surpasses YOLOv8 in Precision, Recall, mAP, and F1 Score indicators by 3.1%, 3.4%, 3.2%, and 3.2%, respectively, while achieving a detection speed of 145 FPS, meeting real-time detection requirements. On the ADP_MBT dataset, YOLO-DFA demonstrates strong performance in detecting other chip defects and exhibits notable generalization ability. © 2025 Elsevier Ltd
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