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Algorithm for Parking Space Detection in Automatic Parking System Based on CNN-Transformer
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Yulong Wang1, 2, Maonan Weng1, Hui Huang1, Xiaoyi Qin1
Automobile Technology | 2024, (8) : 1 - 6
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Automobile Technology | 2024, (8): 1-6
Algorithm for Parking Space Detection in Automatic Parking System Based on CNN-Transformer
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Yulong Wang1, 2, Maonan Weng1, Hui Huang1, Xiaoyi Qin1
Affiliations
  • 1 Auto Engineering Research Institute, Guangzhou Automobile Group Co., Ltd., Guangzhou 510641
  • 2 State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082
Published: 2024-08-24 doi: 10.19620/j.cnki.1000-3703.20221186
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In order to improve success rate and accuracy of automatic parking, firstly, the input image features were extracted based on Convolutional Neural Network (CNN) model, and then the encoding-decoding mechanism of Transfomer model was used to tile the image features extracted by CNN for calculation and inference. Finally, the target prediction results were obtained by feedforward neural network. In this paper, fisheye images were used to recognize the target. The center point of the parking angle and the center point of the empty parking entrance were expressed by two-dimensional coordinate points, which reduced the redundancy of the output information and optimized the model structure. The test results show that the algorithm can better adapt to different parking space line marking mode and different natural environment, with the recall rate of target perception reaches 98%, and the average error of parking space corner center location is less than 3 cm, which meets the requirements of real-time application for robustness, real-time and accuracy.

Automatic parking  /  Parking space detection  /  Visual enhancement  /  Convolutional Neural Network (CNN)  /  Transformer
Yulong Wang, Maonan Weng, Hui Huang, Xiaoyi Qin. Algorithm for Parking Space Detection in Automatic Parking System Based on CNN-Transformer[J]. Automobile Technology, 2024 , (8) : 1 -6 . DOI: 10.19620/j.cnki.1000-3703.20221186
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doi: 10.19620/j.cnki.1000-3703.20221186
  • Online Date：2025-12-22
  • Published：2024-08-24
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    1 Auto Engineering Research Institute, Guangzhou Automobile Group Co., Ltd., Guangzhou 510641
    2 State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082
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