An Improved Model Based on YOLOv8 for Small Object Detection and Recognition

With the rapid advancement of remote sensing technology, remote sensing images are increasingly being used in applications such as geographical monitoring, disaster warning, and urban planning. However, detecting small objects—such as vehicles and small buildings—in such imagery remains challenging due to complex backgrounds, weak features, and interference from factors like terrain, clouds, and lighting, leading to high rates of missed detections and false alarms. To tackle these issues, this paper proposes an improved YOLOv8-based framework for small object detection in remote sensing images. The enhancements include a multi-scale feature fusion mechanism, optimized data augmentation strategies incorporating super-resolution techniques, and a redesigned loss function that emphasizes small objects. These refinements significantly improve the model’s ability to extract discriminative features and detect small targets against cluttered backgrounds. Experimental results demonstrate superior performance across multiple metrics, including precision, recall, mAP50, and mAP50-95, particularly for challenging categories like small vehicles and buildings. This research not only provides an effective solution to the key technical bottleneck in small object detection, advancing the progress of related algorithms, but also offers important theoretical and practical experience for subsequent work.