BCFPL: binary classification ConvNet based fast parking space recognition with low resolution image

Shuo Zhang; Xin Chen; Zixuan Wang

doi:10.1117/12.3034123

13 June 2024 BCFPL: binary classification ConvNet based fast parking space recognition with low resolution image

Shuo Zhang, Xin Chen, Zixuan Wang

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131805T (2024) https://doi.org/10.1117/12.3034123
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

The automobile plays an important role in the economic activities of mankind, especially in the metropolis. Under the circumstances, the demand of quick search for available parking spaces has become a major concern for the automobile drivers. Meanwhile, the public sense of privacy is also awaking, the image-based parking space recognition methods lack the attention of privacy protection. In this paper, we proposed a binary convolutional neural network with lightweight design structure named BCFPL, which can be used to train with low-resolution parking space images and offer a reasonable recognition result. The images of parking space were collected from various complex environments, including different weather, occlusion conditions, and various camera angles. We conducted the training and testing progresses among different datasets and partial subsets. The experimental results show that the accuracy of BCFPL does not decrease compared with the original resolution image directly, and can reach the average level of the existing mainstream method. BCFPL also has low hardware requirements and fast recognition speed while meeting the privacy requirements, so it has application potential in intelligent city construction and automatic driving field.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shuo Zhang, Xin Chen, and Zixuan Wang "BCFPL: binary classification ConvNet based fast parking space recognition with low resolution image", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131805T (13 June 2024); https://doi.org/10.1117/12.3034123

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KEYWORDS

Image resolution

Binary data

Convolution

Image processing

Overfitting

Image classification

Feature extraction

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