BCFPL: Binary classification ConvNet based Fast Parking space recognition with Low resolution image

TL;DR

This paper introduces BCFPL, a lightweight binary ConvNet for fast, privacy-preserving parking space recognition using low-resolution images, suitable for intelligent city and autonomous driving applications.

Contribution

The paper presents a novel low-resolution, lightweight binary ConvNet architecture that maintains accuracy and speed, addressing privacy concerns in parking space recognition.

Findings

BCFPL achieves comparable accuracy to high-resolution methods.

It requires low hardware resources and offers fast recognition.

Effective in complex environments with weather and occlusion.

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.