关键词: YOLOv8n, 小目标检测, 感受野注意力卷积, 动态头, 归一化Wasserstein距离

Abstract:

Aiming at the problems of complex background， insufficient feature extraction ability， small target size， difficult detection， and missed detection in target detection of unmanned aerial vehicles from high-altitude view， an improved target detection algorithm based on YOLOV8n for unmanned aerial vehicles from high-altitude view was proposed. Firstly， the network structure was optimized， and small target perception ability was improved by adding P2 small target detection layer and deleting P5 large target detection layer. Secondly， Receptive Field Attention Convolution （RFAConv） was introduced to improve the Bottleneck of C2f， and the capabilities of feature extraction and fusion were enhanced from spatial dimension. Thirdly， in order to enhance the capabilities of expression and generalization， Dynamic head （Dyhead） module was introduced into Detect detection head. Finally， Normalized Wasserstein Distance （NWD） was used in bounding box similarity measurement to reduce scale sensitivity. The improved YOLOv8n， YOLOv9t and YOLOv10n increase the Average Precision （AP） by 15.6%， 16.7% and 31.0%， respectively， on Visdrone2019 dataset. The detection results on SAR Ship Detection Dataset （SSDD） confirm that the improved algorithm has a strong generalization capability and is more robust. It can be seen that the improved algorithm enhances small target feature extraction and fusion capabilities and has better detection effects in small target detection.

Key words: YOLOv8n, small object detection, Receptive Field Attention Convolution (RFAConv), Dynamic head (Dyhead), Normalized Wasserstein Distance (NWD)