Optimization of Multi-Scale Feature Extraction and Loss Functions in YOLOv8 for Insulator Defect Detection

To address the challenges of high miss detection rates and accuracy degradation in UAV-based insulator defect detection—primarily stemming from complex background interference and the loss of fine-grained features—this paper presents an optimized lightweight detection framework based on an improved YOLOv8 model. The integration of a Spatial-to-Depth Convolution (SPDConv) module strengthens the extraction of fine-grained features for microscopic defects, while the incorporation of an SCConv module suppresses computational redundancy, leading to a 2.80% accuracy improvement. This architecture is further enhanced by a Channel and Spatial Reconstruction Attention Module (CSRAM), which dynamically prioritizes target-related regions and mitigates noise from vegetation and infrastructure. To improve regression robustness against low-quality annotations and blurred boundaries, a Focal-WIoU loss function utilizing a dynamic non-monotonic focusing mechanism is introduced. Experimental results on complex insulator datasets demonstrate that the proposed model achieves an mAP@0.5 of 91.75% and an mAP@0.5:0.95 of 59.86%, representing a 4.40% and 5.04% increase over the YOLOv8 baseline, respectively. Notably, while maintaining a lightweight profile with only 11.14 M parameters and 28.66 G FLOPs, the model achieves a high inference speed of 376.56 FPS, effectively enabling precise multi-scale defect recognition under extreme operational conditions.