MAGC-YOLO:Small Object Detection in Remote Sensing Images based on Multi-scale Attention and Graph Convolution

Jianquan Ouyang, Lingtao Zeng

Abstract: To address the challenge of detecting small targets caused by the small size and high quantity of targets in current unmanned aerial vehicle (UAV) aerial images, we propose a novel multi-scale self-attention graph neural network model based on YOLOv8. This model can learn the weak semantic information generated between small objects, guiding the network to estimate reliable details of small objects and capture relationships between them, thereby achieving accurate detection and classification of small objects through enhancing similar features. Additionally, we introduce an improved objective box loss function to tackle the issue of high-density object detection. We evaluate our proposed model on the widely-used open-source dataset Visdrone2019 and DOTAv2. Experimental results demonstrate that our model outperforms the existing baseline YOLOv8, achieving a significant improvement of 7.40% in terms of mAP50. Ablation experiments further validate the effectiveness of our designed modules and loss function. Furthermore, our approach can efficiently detect small objects in complex road traffic environments, contributing to the advancement of smart city development.

External IDs:dblp:conf/ijcnn/OuyangZ24