Research on target detection based on an improved YOLOv3 SPP algorithm

DOI:	10.11809/bqzbgcxb2023.04.038
Keywords:	target detection; data set; data augmentation; YOLOv3 algorithm; K means+〖KG-*2〗+ clustering algorithm
Abstract:	To better detect and investigate military targets in complex battlefield environments, this paper proposes an improved algorithm based on YOLOv3 SPP. By collecting military objects such as tanks, infantry fighting vehicles and radars of different target sizes and categories, a small data set of military targets is constructed. Then, data enhancement is performed on the data set, the number of samples is expanded, and the robustness of the training model is improved. D IoU and Focal Loss are used to replace the mean square error function and the cross entropy function to improve the accuracy of the target detection algorithm. The K means++ clustering algorithm is used to calculate the applicable anchor frame, which further improves the model detection accuracy. The experimental results show that, compared with the original YOLOv3 SPP algorithm, the improved YOLOv3 SPP military target detection algorithm has faster model convergence, with 10% higher average precision, 9% higher precision and 8% higher recall rate. With good detection ability, it can provide technical support for the detection and reconnaissance tasks of military targets in the battlefield environment.
Issue:	Vol. 44 No. 4 (2023)
Published:	2023-04-28
PDF HTML