Aircraft Skin Defect Detection Based on Dual-Domain Wavelet Feature Enhancement

Accurate detection of aircraft skin surface defects is critical for aviation safety. However, conventional networks suffer from high-frequency edge and texture degradation during downsampling when detecting small-object defects, and achieving structural compactness remains challenging. To address these issues, a lightweight dual-domain wavelet feature-enhanced defect detection algorithm, DEDW-YOLO, is proposed. First, a Dual-Domain Wavelet Downsampling (DDWD) module synergistically couples frequency-domain Haar wavelet transforms with spatial-domain space-to-depth convolutions to mitigate small-object information loss. Second, the C3K2_DE module integrates Distribution Shifting Convolution and Efficient Channel Attention to minimize computational redundancy while preserving feature extraction capabilities. Furthermore, the network architecture is reconfigured alongside the introduction of a Lightweight Shared Convolutional Detection Head (LSCD) to optimize small-object localization precision. Finally, the WIoUv3 loss function enhances bounding box regression quality. Experimental results on a self-constructed skin defect dataset demonstrate that DEDW-YOLO achieves an mAP@0.5 of 87.5% (a 2.6% improvement over the baseline), while reducing parameters, model size, and computational cost by 83.8%, 71.7%, and 37.5%, respectively, successfully balancing high-precision small-object detection and model lightweightness.