[1]CHEN Lichao,CHAO Xin,PAN Lihu,et al.Fine-grained vehicle-type identification based on partially-focused DenseNet[J].CAAI Transactions on Intelligent Systems,2022,17(2):402-410.[doi:10.11992/tis.202012012]
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Fine-grained vehicle-type identification based on partially-focused DenseNet

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 2 Page number: 402-410 Column: 学术论文—人工智能基础 Public date: 2022-03-05
Title:
Fine-grained vehicle-type identification based on partially-focused DenseNet
Author(s):
CHEN Lichao1;  CHAO Xin1;  PAN Lihu1;  CAO Jianfang1; 2;  ZHANG Rui1
1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China;
2. Department of Computer Science and Technology, Xinzhou Teachers University, Xinzhou 034000, China
Keywords:
fine-grained vehicle type identification; part focus; dense connection network; independent component; data enhancement; deep learning; feature extraction; reuse of characteristics
CLC:
TP391
DOI:
10.11992/tis.202012012
Abstract:
Given that fine-grained model recognition rates are low and are mainly concentrated in the diagnostic model difference between parts and that deep learning cannot effectively concern parts, we put forward a fine-grained recognition model—the partially-focused DenseNet. The model can be classified effectively based on its discriminative parts, such as lights and marks of fine-grained vehicle models. First, through the Process Layer, the vehicle part information is repeatedly extracted, and the maximum pool sampling is carried out to obtain more vehicle part information. Then, feature channels are further extracted by multiplexing through dense convolution, and the independent component layer is embedded before dense convolution to obtain relatively independent neurons. This enhances network independence and improves the convergence limit of the model. Experiments show that the model’s recognition accuracy, recall rate, and F1 on the Stanford cars-196 data set reach 95.0%, 94.9%, and 94.8%, respectively, which are higher than the classic convolutional neural network and have a smaller number of parameters. Compared with other methods, the highest accuracy rate is achieved, verifying the effectiveness of the vehicle recognition model.
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