Accurate and efficient wood defect detection is crucial for improving timber utilization rates in the wood processing industry. To address the limitations of existing detection models in terms of accuracy, efficiency, and generalization, this study proposes EFCW-YOLO, an improved model based on YOLOv8n. The model incorporates four key enhancements: the Focal Modulation module for superior context modeling, the Efficient Channel Attention (ECA) mechanism for lightweight channel attention, the C2fCIB module for efficient feature extraction, and the Wise-IoU v3 loss function for robust bounding box regression. Experimental results on the self-built PlankDef-9K multi-species defect dataset demonstrate that EFCW-YOLO achieves a mean average precision (mAP50) of 90.26%, outperforming several mainstream detectors while maintaining a compact model size of 6.12 MB. Furthermore, the model exhibits strong generalization capability, as validated on a cross-domain crop pest dataset. The study concludes that EFCW-YOLO provides a high-performance, lightweight solution for automated wood surface defect detection, offering significant potential for industrial deployment to reduce waste and enhance processing efficiency. This technology holds significant practical potential for enabling precise utilization of wood resources, reducing raw material waste, and enhancing the economic benefits of the wood processing industry.