Research on Armored Vehicle Detection Algorithm Based on Visible and Infrared Image Fusion
The armored vehicle detection algorithm based on visible images is easily interfered by the complex ground environment.An armored vehicle detection algorithm based on fusion of visible and infrared images is proposed.The features of visible image and infrared image are adaptively fused by a convolutional neural network,which improves the detection accuracy of armored vehicle in complex ground environment.A visible-thermal armored vehicle(VTAV)dataset is constructed through on-site photography for the detection task of armored vehicle in complex ground environment.Based on the classic one-stage anchor-free detection algorithm,three fusion structures called early feature fusion,middle feature fusion and late feature fusion,are designed,and two different fusion methods are proposed.The detection performances of different fusion structures and fusion methods are compared on the VTAV dataset.The experimental results show that the peroformce of late feature fusion structure is the best,and compared to the armored vehicle detection algorithm based on visible image,mAP@0.5:0.95 is increased by 2.6％.The armored vehicle detection algorithm based on fusion of visible and infrared images has been proven to effectively improve the detection accuracy in complex ground environment.
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