Industrial IoVT (Internet of Video Things) still faces the dual bottleneck of insufficient accuracy and poor real-time performance in circuit board tiny defect detection. To this end, we propose RGM-YOLO (RefConv–GhostNet–CBAM-enhanced YOLOv8 ), which introduces deformable convolution and channel attention via RefConv and GhostNet modules, and experimentally validates it on the BDL-PCB (Bare Die on Laminate–Printed Circuit Board) large-scale dataset. Experimental results show that RGM-YOLO achieves 94.2% in mAP50 and 67.3% in mAP90–95, representing improvements of 2.4% and 11.2% over the baseline model, YOLOv8. The number of parameters and GFLOPs is reduced by 4.2M and 2.5G, respectively, while the FPS increases from 78 to 102. This approach offers a high-precision, low-latency defect detection paradigm for edge IoVT devices targeting small defects and can be generalized to other industrial quality-inspection scenarios.