<p>Defect detection plays a crucial role in chip production and is essential for ensuring the quality of electronic products. However, existing methods still face challenges when detecting small-sized targets. To address this issue, we propose a chip solder joint defect detection model, SSF-YOLO, based on YOLOv8. A lightweight feature extraction module, StarNet-SimAM Block, is designed to enhance feature extraction capability while reducing the complexity of the model’s feature extraction network, thereby improving detection efficiency. We also introduce a SCAM Fusion module that incorporates a separate channel attention mechanism to fuse feature information from different levels of the backbone network, thereby mitigating the loss of small defect features during feature fusion. A position regression loss function, NWD-CIOU, is employed to calculate the distance between the bounding box of small targets and the ground truth, effectively reducing the missed detection rate for small defects. Experiments conducted on two datasets, SMT chip solder joint defects dataset and PKU-Market-PCB dataset, demonstrate that SSF-YOLO improves the mAP metric by 1.5% and 2.3%, respectively, compared with YOLOv8. The detection speed on SMT chip solder joint defects dataset reaches 128 FPS, representing a 6.7% improvement over YOLOv8, indicating enhanced detection accuracy and real-time detection capability.</p>