Improving Detection Capabilities of YOLOv8-n for Small Objects in Remote Sensing Imagery: Towards Better Precision with Simplified Model Complexity

Abstract

Abstract This study presents a comprehensive analysis and improvement of the YOLOv8-n algorithm for object detection, focusing on the integration of Wasserstein Distance Loss, FasterNext, and Context Aggravation strategies. Through a detailed ablation study, each strategy was systematically evaluated individually and collectively to assess its contribution to the model's performance. The results indicate that each strategy uniquely enhances the model's performance, significantly increasing mAP and reducing model complexity when all three are integrated. Visualizations through Grad-CAM further substantiate the improved model's capacity to extract and focus on key object features. Comparisons with existing models, such as YOLOv5-n, YOLOv5-s, YOLOX-n, YOLOX-s, and YOLOv7-tiny, the improved YOLOv8-n model achieves an optimal balance between accuracy and model complexity, outperforming other models in terms of model accuracy, model complexity, and model inference speed. Further image inference tests validate the model's performance, showcasing its superior detection capabilities.