  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.
