TY - GEN
T1 - Fast Parking Slot Detection in the Bird's Eye View
AU - Wan, Chenglin
AU - Wang, Weida
AU - Yang, Chao
AU - Xiang, Changle
AU - Li, Ying
N1 - Publisher Copyright:
© 2023, Beijing HIWING Sci. and Tech. Info Inst.
PY - 2023
Y1 - 2023
N2 - Vision-based parking slot detection plays an important role for autonomous vehicles to achieve automatic parking. Complex visual environments severely affect the accuracy of parking slot detection and occupancy classification, such as light, weather, shadows, and ground textures and so on. To solve this problem, we propose a deep learning-based fast parking slot detection method in the bird's eye view image, namely FPS-Net. Firstly, given a bird's eye view, a parking slot detection method based on MobileNetv3 is proposed to predict the location, shape and orientation of the marking points. Secondly, the four corner points of the parking slot are inferred by post-processing. Finally, a parking slot is determined as vacant or not based on the distribution of extracted features using HOG feature extraction. From the experimental results it can be seen that the FPS-Net can identify various types of parking slots with an average precision of 98.34% in the PS2.0 dataset and achieve 87.39% accuracy for occupation classification.
AB - Vision-based parking slot detection plays an important role for autonomous vehicles to achieve automatic parking. Complex visual environments severely affect the accuracy of parking slot detection and occupancy classification, such as light, weather, shadows, and ground textures and so on. To solve this problem, we propose a deep learning-based fast parking slot detection method in the bird's eye view image, namely FPS-Net. Firstly, given a bird's eye view, a parking slot detection method based on MobileNetv3 is proposed to predict the location, shape and orientation of the marking points. Secondly, the four corner points of the parking slot are inferred by post-processing. Finally, a parking slot is determined as vacant or not based on the distribution of extracted features using HOG feature extraction. From the experimental results it can be seen that the FPS-Net can identify various types of parking slots with an average precision of 98.34% in the PS2.0 dataset and achieve 87.39% accuracy for occupation classification.
KW - Deep convolutional neural network
KW - Histograms of oriented gradients
KW - Occupation classification
KW - Parking slot detection
UR - http://www.scopus.com/inward/record.url?scp=85151052042&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-0479-2_107
DO - 10.1007/978-981-99-0479-2_107
M3 - Conference contribution
AN - SCOPUS:85151052042
SN - 9789819904785
T3 - Lecture Notes in Electrical Engineering
SP - 1183
EP - 1193
BT - Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022
A2 - Fu, Wenxing
A2 - Gu, Mancang
A2 - Niu, Yifeng
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Autonomous Unmanned Systems, ICAUS 2022
Y2 - 23 September 2022 through 25 September 2022
ER -