Pedestrian and vehicle identification from the perspective of unmanned aerial vehicles based on ST YOLOv7

DOI:	10.11809/bqzbgcxb2024.03.038
Keywords:	complex background; remote small targets; YOLOv7; attention mechanism; target recognition
Abstract:	Because of the complex background from the perspective of unmanned aerial vehicle (UAV), most of the identified targets are remote small targets, which easily leads to missed detection and false detection. In order to achieve high precision recognition of pedestrians and vehicles from the perspective of UAV, ST YOLOv7 algorithm based on YOLOv7 network model is proposed. Swin Transform module is integrated into the backbone network to construct the global relationship between complex background and small targets, and SENet channel attention mechanism is integrated to assign different weights to different channel features to enhance the capture of small target features. In the head network, C3 module in YOLOv5 network is added to increase the depth and receptive field of the network, improve the ability of feature extraction, and add a small target detection layer to further improve the accuracy of small target recognition. Experiments show that the ST YOLOv7 network model has a high recognition accuracy of 83.4% for pedestrians in self made aerial photography datasets, and the recognition accuracy of vehicles in the dataset reaches 89.3%. Both of them are superior to YOLOv5 and YOLOv7 target detection algorithms, and achieve higher accuracy with less efficiency loss.
Issue:	Vol. 45 No. 3 (2024)
Published:	2024-03-28
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