YOLO-DST: MEMS Small-Object Defect Detection Method Based on Dynamic Channel–Spatial Modeling and Multi-Attention Fusion

During the process of defect detection in Micro-Electro-Mechanical Systems (MEMSs), there are many problems with the metallographic images, such as complex backgrounds, strong texture interference, and blurred defect edges. As a result, bond wire breaks and internal cavity contaminants are difficult to effectively identify, which seriously affects the reliability of the whole machine. To solve this problem, this paper proposes a MEMS small-object defect detection method, YOLO-DST (Dynamic Channel-Spatial Modeling and Triplet Attention-based YOLO), based on dynamic channel-spatial blocks and multi-attention fusion. Based on the YOLOv8s framework, the proposed method integrates dynamic channel-space blocks into the backbone and detection head to enhance feature representation across multiple defect scales. The neck of the network integrates multiple triple attention mechanisms, effectively suppressing the background interference caused by complex metallographic textures. Combined with the small-object perception enhancement network based on a Transformer, this method improves the capture ability and stability of the model for the detection of bond wire breaks and internal cavity contaminants. In the verification stage, a MEMS small-object defect dataset covering typical metallographic imaging was constructed. Through comparative experiments with the existing mainstream detection models, the results showed that YOLO-DST achieved better performance in indicators such as Precision and mAP@50%.