基于优化卷积神经网络的车辆特征识别算法研究

doi:10.11959/j.issn.1000-0801.2023188

Abstract

Abstract:

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

CLC Number:

TP391.14

Xuan CHEN, Jiyi WU. Research on vehicle feature recognition algorithm based on optimized convolutional neural network[J]. Telecommunications Science, 2023, 39(10): 101-111.

Figures/Tables 12

References 21

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算法	sedan	SUV	truck	bus	minivan	microbus
本文算法	96.32%	95.61%	92.14%	94.32%	93.89%	94.78%
CNN	78.38%	82.41%	79.23%	81.92%	82.14%	81.62%
R-CNN	84.28%	87.29%	81.82%	85.23%	86.24%	86.19%
ABC-CNN	90.73%	89.31%	88.29%	93.02%	91.21%	92.18%
Faster R-CNN	91.21%	88.16%	89.27%	93.32%	91.82%	92.91%
AlexNet	92.78%	91.40%	90.75%	93.46%	92.04%	93.01%
VGG16	94.19%	93.39%	91.74%	93.52%	92.76%	93.85%
YOLOV8	95.12%	94.02%	91.84%	93.75%	92.79%	93.92%

算法	sedan	SUV	truck	bus	minivan	microbus
本文算法	92.13%	90.13%	90.23%	91.32%	90.41%	92.38%
CNN	76.18%	79.31%	75.73%	78.93%	78.34%	75.32%
R-CNN	81.72%	85.17%	78.92%	82.43%	83.14%	83.27%
ABC-CNN	89.34%	88.48%	87.91%	89.03%	88.71%	89.31%
Faster R-CNN	88.31%	89.27%	83.71%	88.92%	87.31%	88.93%
AlexNet	90.78%	89.40%	84.75%	89.47%	88.34%	89.17%
VGG16	91.24%	89.42%	87.32%	90.43%	89.04%	91.05%
YOLOV8	91.39%	90.01%	88.19%	90.47%	89.13%	91.38%

算法	sedan	SUV	truck	bus	minivan	microbus
本文算法	12.183	13.238	11.147	11.611	12.523	12.416
CNN	15.982	15.298	14.327	14.317	15.925	15.291
R-CNN	14.925	14.198	13.782	12.893	14.197	14.981
ABC-CNN	13.891	13.278	12.982	12.892	13.183	13.261
Faster R-CNN	13.782	14.372	12.931	12.927	13.894	13.936
AlexNet	13.153	14.176	12.369	12.725	13.126	13.619
VGG16	13.013	13.739	12.319	12.074	13.015	12.946
YOLOV8	12.186	13.341	11.159	11.657	12.576	12.519

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

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