Abstract To address the challenges of significant changes in the field of view and complex spatiotemporal information in unmanned aerial vehicle aerial image target detection, a model for small object detection in aerial photography based on low dimensional image feature fusion is presented grounded on the YOLOv5(you only look once version 5) architecture. Coordinate attention is introduced to improve the inverted residuals of MobileNetV3, thereby increasing the spatial dimension information of images while reducing parameters of the model. The YOLOv5 feature pyramid network structure is improved to incorporate feature images from shallow networks. The ability of the model to represent low-dimensional effective information of images is enhanced, and consequently the detection accuracy of the proposed model for small objects is improved. To reduce the impact of complex background in the image, the parameter-free average attention module is introduced to focus on both spatial attention and channel attention. VariFocal Loss is adopted to reduce the weight proportion of negative samples in the training process. Experiments on VisDrone dataset demonstrate the effectiveness of the proposed model. The detection accuracy is effectively improved while the model complexity is significantly reduced.
Fund:National Natural Science Foundation of China(No.92367109), Young Scientists Fund of National Natural Science Foundation of China(No.62301163)
Corresponding Authors:
HUANG Jie, Ph.D., professor. His research interests include pa-ttern recognition, intelligent system and multi-agent system.
About author:: CAI Fenghuang, Ph.D., professor. His research interests include machine learning and power electronic converter control. ZHANG Jiaxiang, Master student. His research interests include intelligent vision and unmanned system.
CAI Fenghuang,ZHANG Jiaxiang,HUANG Jie. Model for Small Object Detection in Aerial Photography Based on Low Dimensional Image Feature Fusion[J]. Pattern Recognition and Artificial Intelligence, 2024, 37(2): 162-171.
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