SOD-YOLOv8n: Small Object Detection in Remote Sensing Images Based on YOLOv8n

Small target detection in remote sensing images is a significant research focus within the remote sensing domain. Recently, various YOLO algorithms have demonstrated remarkable achievements in the detection of small targets in remote sensing. However, YOLO-based detection algorithms still face challenges in this context, including limited feature expression capacity, difficulties in mitigating aliasing effects, and inadequate adaptability to complex-shaped targets. To address these issues, this letter proposes a novel object detection network SOD-YOLOv8n. First, we propose a novel multipath feature fusion module (MFFM), which enhances feature extraction through diverse dimensional feature processing strategies (global, local, channel, and spatial). It also fuses complementary information across channels via channel shuffling, thereby augmenting feature representation capabilities and boosting the detection accuracy of small targets in remote sensing. Second, we design an anti-aliasing module (AAM) that uses wavelet pooling technology for frequency decomposition to mitigate the aliasing effect generated during model downsampling, thereby better retaining the key high-frequency information of small targets in remote sensing. Finally, we introduce the shape-aware intersection over union (Shape-IoU) loss function, which emphasizes the edge features of the target shape (such as contours and curvature) by calculating the similarity between the true target and the target shape in the predicted box, so as to better match targets with complex shapes. We conduct extensive experiments on the AI-TOD and USOD remote sensing small target datasets, and the results show that SOD-YOLOv8n outperforms several established state-of-the-art detection models.