Lightweight YOLOv8-Obb Optimization with Hybrid Attention and Dynamic Feature Reconstruction for Remote Sensing Object Detection

To address the challenges of multi-scale object recognition and complex background in-terference in remote sensing images, this paper proposes a lightweight improved algo-rithm based on YOLOv8-obb. By integrating a hybrid local-channel attention mechanism (MLCA), dynamic upsampling (DySample), and a reparameterized cross-scale feature aggregation module (RepNCSPELAN), the algorithm achieves dual enhancements in de-tection accuracy and computational efficiency. The MLCA module enhances feature dis-criminability in complex backgrounds through a dual-path mechanism combining local and global pooling. The DySample module employs content-aware sampling point gen-eration to improve feature reconstruction for multi-scale targets. The RepNCSPELAN module reduces model parameters by 29% while preserving cross-scale feature fusion ca-pabilities. Experimental results on the DOTA dataset demonstrate a 2.3% improvement in mAP50, with parameters reduced to 2.2M and FLOPs decreased by 27%. Cross-dataset validation on DIOR further confirms a 1.5% mAP50 gain. Compared to mainstream lightweight models (e.g., YOLOv5n, YOLOv8n), the proposed algorithm exhibits superior performance in accuracy (65.3% vs. 60.8%), parameter efficiency (2.2M vs. 3.1M), and computational cost (6.2G vs. 8.5G FLOPs). Ablation studies validate the efficacy of each module, while visualizations highlight robustness in dense small-object detection and ro-tated target localization. This work provides an efficient solution for real-time remote sensing object detection in complex scenarios and offers a novel technical pathway for lightweight deep learning model design.