Tank vehicle detection method based on improved YOLOv7 tiny

DOI:	10.11809/bqzbgcxb2023.12.038
Keywords:	object detection; YOLOv7 tiny network; asymmetric convolutions; 3D attention mechanism; WIoU loss
Abstract:	Aiming at problems such as the ineffective and slow speed of the tank vehicle detection algorithm caused by the large difference in the aerial photography height of UAV, we proposed a tank vehicle detection algorithm from UAV perspective based improved YOLOv7 tiny. In terms of data sets, a tank vehicle dataset containing 568 images and 2 132 targets was constructed. In terms of algorithms,an AC ELAN structure was proposed to enhance image feature recognition and a 3D attention mechanism was incorporated to improve the ability to extract target information; The SPPCSPC structure was introduced to further expand the receptive field, at the same time, it can also effectively reduce the training and learning time; The loss function calculation method was replaced by WIoU, which focuses on the common quality anchor box, and this method accelerates the model convergence.The experimental results show that the improved algorithm in this paper performs well on the self built dataset, compared with the traditional YOLOv7 tiny, the average precision is increased by 5.0%. The detection speed on the GPU device reaches 71 FPS, the experimental results illustrate that our algorithms can achieve real time detection on UAV computing platforms.
Issue:	Vol. 44 No. 12 (2023)
Published:	2023-12-28
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