Vehicle Type Recognition Algorithm Based on Improved YOLOv7x
In order to improve the accuracy of vehicle type recognition and improve the problem of false detection,this paper proposes an improved YOLOv7x vehicle type recognition algorithm.Firstly,this paper extracts the BIT-Vehicle dataset and divides it manually to make the dataset meet the requirements of the model;Secondly,it adds CBAM attention mechanism to backbone network to improve the feature extraction ability of backbone network with small increase of model parameters;Finally,it introduces the SIoU loss function,redefines the loss function and improves the mAP0.5 and precision of model detection by utilizing the vector angle between the predict box and the groundtruth box.The experimental results show that improved YOLOv7x algorithm is better than YOLOv7x algorithm.For the improved YOLOv7x algorithm,mAP0.5 and precision are 98.4%and 97.4%,increase by 0.5%and 1.1%respectively,improved YOLOv7x vehicle type recognition algorithm has better accuracy and lower false detection rate,which better meets the needs of vehicle recognition.
YOLOv7xvehicle type recognitionattention mechanism
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