Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 165-173.DOI: 10.3778/j.issn.1002-8331.2007-0230

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Compression of UAV Object Detection Model Based on Improved YOLOv3-SPP

HUANG Wenbin, CHEN Renwen, YUAN Tingting   

  1. State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Online:2021-11-01 Published:2021-11-04



  1. 南京航空航天大学 机械结构力学及控制国家重点实验室,南京 210016


Due to the limited memory and computing power of UAV equipment, it is very challenging inrunning deep-learning models on such devices for object detection. Dense and small sizes objects in aerial image and datasets increases the difficulty on aerial target recognition and classification. To deal with these challenges, the YOLOv3-SPP model is improved by using GIoU instead of the mean-square error, which raise the positioning accuracy if objects. A method of data enhancement is also proposed. It balances categories number by weighted sampling specific categories. During training, different scene pictures are randomly combined to form batch training, which increases detection robustness. Then, the model is compressed. On the basis of sparse training and channel pruning by adding scaling factor to BN layer, the importance of residual layer is measured by scaling factor, and unimportant residual is pruned to further reduce the number of forward reasoning layers and parameters. In experiment, model parameters are reduced by 95.7%, the module size is reduced by 95.82%, and the speed is increased by 3 times. The accuracy and speed are higher than the latest YOLOv5 series lightweight model.

Key words: object detection, UAVs, channel pruning, model compression, YOLOv3



关键词: 目标检测, 无人机, 通道剪枝, 模型压缩, YOLOv3