Telecommunications Science ›› 2023, Vol. 39 ›› Issue (10): 101-111.doi: 10.11959/j.issn.1000-0801.2023188

• Research and Development • Previous Articles    

Research on vehicle feature recognition algorithm based on optimized convolutional neural network

Xuan CHEN1, Jiyi WU2,3   

  1. 1 Zhejiang Industry Polytechnic College, Shaoxing 312000, China
    2 Zhejiang Federation of Artificial Intelligence, Hangzhou 310027, China
    3 Intelligent Education Research Center, Zhejiang University, Hangzhou 310027, China
  • Revised:2023-10-10 Online:2023-10-01 Published:2023-10-01
  • Supported by:
    The National Natural Science Foundation of China(61702151);The National Natural Science Foundation of China(61702320);The National Natural Science Foundation of China(61772334);The National Key Research and Development Program of China(2018YFB1003800);Zhejiang Provincial Philosophy and Social Sciences Planning Project(23NDJC369YB)


To address the issue of weak identification and low accuracy in recognizing features of target vehicles at different distances in road scene images, a vehicle feature recognition algorithm based on optimized convolutional neural network (CNN) was proposed.Firstly, a multi-scale input based on the PAN model was employed to capture target vehicle features at varying distances.Subsequently, improvements were made to the network model by incorporating multi-pool, batch normalization (BN) layers, and Leaky ReLU activation functions within the CNN architecture.Furthermore, the generalization ability of the network model was enhanced by introducing a hybrid attention mechanism that focuses on important features and regions in the vehicle image.Lastly, a multi-level CNN structure was constructed to achieve feature recognition for vehicles.Simulation experiment results conducted on the BIT-Vehicle database within a single scene show the proposed algorithm’s significant enhancements in single-object and multi-object recognition rates compared to CNN, R-CNN, ABC-CNN, Faster R-CNN, AlexNet, VGG16, and YOLOV8.Specifically, improvements of 16.75%, 10.9%, 4%, 3.7%, 2.46%, 1.3%, and 1% in single-object recognition, as well as 17.8%, 10.5%, 2.5%, 3.8%, 2.7%, 1.1%, and 1.3% in multi-object recognition, have been demonstrated by the proposed algorithm, respectively.Over the more complex UA-DETRAC datasets, more precise results have been also achieved by the proposed algorithm in recognizing target vehicles at various distances compared to other algorithms.

Key words: vehicle recognition, convolutional neural network, multi-scale input

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