LGC-YOLO: Local-Global Feature Extraction and Coordination Network with Contextual Interaction for Remote Sensing Object Detection

Object detection in high-resolution remote sensing image (HRRSI) faces great challenges of large-scale variations in object size, densely distributed small objects, and complex background interferences. To address these challenges, we propose an innovative single-stage Local-Global Feature Extraction and Coordination Network (LGC-YOLO) to improve the detection accuracy of objects in HRRSIs. LGC-YOLO mainly comprises three modules of Local-Global Spatial Feature Extraction (LGSFE), Gradient Optimized Spatial Information Interaction (GOSII), and Edge-Semantic Feature Coordination Fusion(ESFCF), which synergistically improves the feature extraction and object detection capabilities of LGC-YOLO. Firstly, LGSFE captures local and global features of dense objects through RFAConv and global pooling in multi-branch structure, which effectively alleviates the misalignment between the extracted features of objects and their intrinsic characteristics, thereby providing more accurate and abundant features for subsequent object detection. Secondly, GOSII is designed by dynamically adjusting the weights of each feature channel through combining SRU blocks and SimAM attention mechanism, which are further optimized and embedded into C2f to enhance the representation ability of contextual features. GOSII captures crucial features from complex backgrounds and improves information transmission. Finally, ESFCF integrates the edge and semantic information within shallow feature maps to address the issue of inaccurate localization for small objects, and further improves object detection accuracy by compensating for the loss of edge details in feature extraction. Extensive experiments on three commonly used remote sensing datasets of NWPU VHR-10, VisDrone 2019, and DOTA demonstrate the superiority of our method in object classification and localization compared to other state-of-the-art methods.