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 comer center location is less than 3 cm,which meets the requirements of real-time application for robustness,real-time and accuracy.
关键词
自动泊车/车位检测/视觉增强/卷积神经网络/Transformer
Key words
Automatic parking/Parking space detection/Visual enhancement/Convolutional Neural Network(CNN)/Transformer
维普
万方数据