MFFCI–YOLOv8: A Lightweight Remote Sensing Object Detection Network Based on Multiscale Features Fusion and Context Information

Most current researches primarily focus on improving experimental accuracy using large models, often neglecting the deployment challenges. There is a growing need for lightweight algorithms in certain remote sensing devices. Moreover, remote sensing images (RSIs) often contain numerous small, densely distributed targets, which pose significant detection challenges. To address these issues, we have improved the YOLOv8s network and developed a lightweight remote sensing object detection (RSOD) network based on multiscale features fusion and context information (MFFCI–YOLOv8). This network combines multiscale feature fusion and contextual information to accurately detect objects in RSIs. First, we introduce the lightweight CSP bottleneck with attention module, which utilizes partial convolution calculation and SimAM attention mechanisms to decrease the number of parameters and computational complexity while enhancing feature extraction capabilities. Second, we design the gate spatial pyramid pooling fast module to enhance the model's perception of scale and contextual information, thus improving the detection of small objects. Last, we employ the multiscale fusion lightweight neck module for more efficient multiscale feature fusion, preventing the loss of small objects. Compared to YOLOv8s, our overall model reduces the number of parameters by 7.7% and FLOPs by 11.9%. We validated the accuracy of MFFCI–YOLOv8 on two remote sensing datasets, NWPU VHR-10 and VisDrone. The experimental results demonstrate that our model offers a low computational cost and high detection accuracy compared to other RSOD models and other YOLO models.