Integrating Multi-Scale Feature Extraction and Skew-IoU Optimization for Real-Time Helicopter Blade Detection

This study tackles the challenges posed by complex outdoor lighting on traditional visual measurement methods for helicopter rotor blade motion parameters. We introduce the Multi-feature Fusion and Self-Attention Rotating Detector (MFSA-RD), a novel detection framework that significantly enhances detection accuracy and efficiency. The model enhances YOLOv5s by integrating an advanced multi-feature extraction module that optimizes feature integration across various scales and positions. It streamlines the network by removing redundant convolution layers and utilizes a multi-head self-attention mechanism coupled with a Cross-Stage Partial (CSP) convolution to effectively manage complex lighting disturbances. Moreover, the model incorporates a Skew Intersection Over Union (SKEWIOU) and angular loss to refine the loss functions, leading to improved detection performance. Extensive evaluations on both a proprietary outdoor rotor blade dataset and the DOTA-v1.0 public dataset demonstrate significant enhancements over the baseline YOLOv5s, with improvements in mean average precision (mAP) and frame rates by up to 12.8\% and 47.7\%, respectively.