Research on vehicle feature recognition algorithm based on optimized convolutional neural network
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 PA...
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| Format: | Article |
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Beijing Xintong Media Co., Ltd
2023-10-01
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| Series: | Dianxin kexue |
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| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023188/ |
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| _version_ | 1841533676792315904 |
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| author | Xuan CHEN Jiyi WU |
| author_facet | Xuan CHEN Jiyi WU |
| author_sort | Xuan CHEN |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-df0446ef5d584e9b93a68986975a7b01 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-10-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-df0446ef5d584e9b93a68986975a7b012025-01-15T02:58:04ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-10-013910111159560191Research on vehicle feature recognition algorithm based on optimized convolutional neural networkXuan CHENJiyi WUTo 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023188/vehicle recognitionconvolutional neural networkmulti-scale input |
| spellingShingle | Xuan CHEN Jiyi WU Research on vehicle feature recognition algorithm based on optimized convolutional neural network Dianxin kexue vehicle recognition convolutional neural network multi-scale input |
| title | Research on vehicle feature recognition algorithm based on optimized convolutional neural network |
| title_full | Research on vehicle feature recognition algorithm based on optimized convolutional neural network |
| title_fullStr | Research on vehicle feature recognition algorithm based on optimized convolutional neural network |
| title_full_unstemmed | Research on vehicle feature recognition algorithm based on optimized convolutional neural network |
| title_short | Research on vehicle feature recognition algorithm based on optimized convolutional neural network |
| title_sort | research on vehicle feature recognition algorithm based on optimized convolutional neural network |
| topic | vehicle recognition convolutional neural network multi-scale input |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023188/ |
| work_keys_str_mv | AT xuanchen researchonvehiclefeaturerecognitionalgorithmbasedonoptimizedconvolutionalneuralnetwork AT jiyiwu researchonvehiclefeaturerecognitionalgorithmbasedonoptimizedconvolutionalneuralnetwork |