To address the challenges of significant size variations, inconspicuous textures, weak edges, and background interference in wire harness insulation surface defect detection, this paper proposes WHD-YOLOv8n, an improved YOLOv8n-based defect detection algorithm that integrates ODConv, CBAM, BiFPN, and Inner-CIoU for adaptive feature extraction, defect-related feature enhancement, multi-scale feature fusion, and bounding-box regression, respectively. WHD-YOLOv8n achieves an mAP@0.5 of 90.15%, representing a 6.21 percentage-point improvement over the YOLOv8n baseline, with Precision, Recall, and F1-score of 92.05%, 89.87%, and 90.95%, respectively. The model has 4.83 M parameters and requires 11.9 GFLOPs, while achieving an inference speed of 135.6 FPS. Ablation experiments and comparisons with representative general-purpose and industrial defect detectors demonstrate the effectiveness of the proposed design and its favorable accuracy–efficiency trade-off. Further experiments under haze and fog degradation show that WHD-YOLOv8n consistently outperforms the baseline under the tested degraded imaging conditions, although severe degradation can still cause missed detections. The proposed method therefore provides an effective solution for wire harness insulation surface defect detection and offers a basis for further research on robust detection under complex industrial imaging conditions.