CIMB-YOLOv8: A Lightweight Remote Sensing Object Detection Network Based on Contextual Information and Multiple Branches

A lightweight YOLOv8 variant, CIMB-YOLOv8, is proposed to address challenges in remote sensing object detection, such as complex backgrounds and multi-scale targets. The method enhances detection accuracy while reducing computational costs through two key innovations: Contextual Multi-branch Fusion: Integrates a space-to-depth multi-branch pyramid (SMP) to capture rich contextual features, improving small target detection by 1.2% on DIOR; Lightweight Architecture: Employs Lightweight GroupNorm Detail-enhance Detection (LGDD) with shared convolution, reducing parameters by 14% compared to YOLOv8n. Extensive experiments on DIOR, DOTA, and NWPU VHR-10 datasets demonstrate the model’s superiority, achieving 68.1% mAP on DOTA (+0.7% over YOLOv8n) and 82.9% mAP on NWPU VHR-10 (+1.7%). The model runs at 118.7 FPS on NVIDIA 3090, making it well-suited for real-time applications on resource-constrained devices. Results highlight its practical value for remote sensing scenarios requiring high-precision and lightweight detection.