GCB YOLOv5s algorithm for real time detection of a low altitude UAV

DOI:	10.11809/bqzbgcxb2023.07.001
Keywords:	low altitude UAV; target identification; YOLOv5s; real time detection; lightweight design
Abstract:	In order to deal with the security threat caused by “black flying” and “indiscriminate flying” of UAVs, it is urgent to carry out effective real time detection and identification of UAVs. However, due to the flexibility, miniaturization and multiple interference factors of low altitude UAVs, the traditional radar detection methods are difficult to deal with. Therefore, based on machine vision detection, this paper proposes a GCB YOLOv5s algorithm for low altitude UAV real time detection. Aiming at the problem that the operation speed of the classical YOLOv5s algorithm is difficult to meet the high definition real time processing, the proposed algorithm uses a lightweight GhostNet network to replace the convolution operation in the YOLOv5s backbone network, which simplifies the network structure and greatly improves the calculation speed. The CA attention mechanism is also introduced in this algorithm. What’s more, the PANet structure of the neck is replaced by the BiFPN bidirectional weighted feature pyramid. Then, the detection accuracy is improved on the basis of the simplification of the network structure. Subsequently, the flight attitudes of UAVs under different complex backgrounds such as buildings, clouds, trees and shadows are photographed. Combining public data sets, the algorithm is trained and tested. The test results show that the GCB YOLOv5s algorithm reduces the number of parameters and the floating point calculation amount by nearly 40%, and achieves an accuracy of 96.7%, a recall rate of 96.4% and an average accuracy of 97.5%.
Issue:	Vol. 44 No. 7 (2023)
Published:	2023-07-28
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