Abstract
Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net. VPS-Net converts the vacant parking slot detection into a two-step problem, including parking slot detection and occupancy classification. In the parking slot detection stage, we propose a parking slot detection method based on YOLOv3, which combines the classification of the parking slot with the localization of marking points so that various parking slots can be directly inferred using geometric cues. In the occupancy classification stage, we design a customized network whose size of convolution kernel and number of layers are adjusted according to the characteristics of the parking slot. Experiments show that VPS-Net can detect various vacant parking slots with a precision rate of 99.63% and a recall rate of 99.31% in the ps2.0 dataset, and has a satisfying generalizability in the PSV dataset. By introducing a multi-object detection network and a classification network, VPS-Net can detect various vacant parking slots robustly.
Highlights
With the rapid development of society, passenger cars are becoming more and more popular in large cities, which makes it difficult to find a vacant parking slot
The images in the ps2.0 dataset are collected from various environmental conditions through an around view monitor (AVM) system with four low-cost fish-eye cameras equipped on a SAIC Roewe
We evaluate the performance of the customized deep convolutional neural network (DCNN) in the self-annotated testing dataset and compare it with the conventional feature extraction and classification technique (HOG+Support Vector Machine (SVM)) [37] and some SOTA networks including AlexNet [49], VGG-16 [50], ResNet-50 [51], and MobileNetV3 [52], in terms of the classification accuracy, the running time, the model size, and the precision-recall curve
Summary
With the rapid development of society, passenger cars are becoming more and more popular in large cities, which makes it difficult to find a vacant parking slot. In total car collisions, 23% of accidents happen in parking lots [2]. In this context, the park assist system (PAS) is a promising technology most drivers want to see, which is composed of three parts: object position designation, path planning, and parking guidance or path tracking. As the most important component of the PAS, the task of the object position designation is to detect a vacant parking slot accurately. The parking slot marking-based approach can be applied in wider situations, since it does not depend on the existence of adjacent vehicles or extra communication equipment. The vacant parking slot detection in the around view image can make full use of the existing equipment on the vehicle
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