Enhanced Multi-Scale Feature Extraction Lightweight Network for Remote Sensing Object Detection

Deep learning has made significant progress in the field of oriented object detection for remote sensing images. However, existing methods still face challenges when dealing with difficult tasks such as multi-scale targets, complex backgrounds, and small objects in remote sensing. Maintaining model lightweight to address resource constraints in remote sensing scenarios while improving task completion for remote sensing tasks remains a research hotspot. Therefore, we propose an enhanced multi-scale feature extraction lightweight network EM-YOLO based on the YOLOv8s architecture, specifically optimized for the characteristics of large target scale variations, diverse orientations, and numerous small objects in remote sensing images. Our innovations lie in two main aspects: First, a dynamic snake convolution (DSC) is introduced into the backbone network to enhance the model’s feature extraction capability for oriented targets. Second, an innovative focusing-diffusion module is designed in the feature fusion neck to effectively integrate multi-scale feature information. Finally, we introduce Layer-Adaptive Sparsity for magnitude-based Pruning (LASP) method to perform lightweight network pruning to better complete tasks in resource-constrained scenarios. Experimental results on the lightweight platform Orin demonstrate that the proposed method significantly outperforms the original YOLOv8s model in oriented remote sensing object detection tasks, and achieves comparable or superior performance to state-of-the-art methods on three authoritative remote sensing datasets (DOTA v1.0, DOTA v1.5, and HRSC2016).