SRM-YOLO for Small Object Detection in Remote Sensing Images
Bin Yao, Chengkun Zhang, Qingxiang Meng, Xiandong Sun, Xuyang Hu, Lu Wang, Xilai Li
Abstract
Small object detection presents significant challenges in computer vision, often affected by factors such as low resolution, dense object distribution, and complex backgrounds, which can lead to false positives or missed detections. In this paper, we introduce SRM-YOLO, a novel small object detection algorithm based on the YOLOv8 framework. The model incorporates the following key innovations: Reuse Fusion Structure (RFS), which enhances feature fusion; SPD-Conv, which enables effective downsampling while preserving critical information; and a specialized detection head designed for small objects. Additionally, the MPDIoU loss function is employed to improve detection accuracy. Experimental results on the VisDrone2019 dataset show that SRM-YOLO significantly enhances detection accuracy, achieving a 5.2% improvement in mAP50 over YOLOv8n. Additionally, its superior performance on the SSDD and NWPU VHR-10 datasets further validates its effectiveness in small object detection tasks.