Abstract
Due to the complex visual environment and incomplete display of parking slots on around-view images, vision-based parking slot detection is a major challenge. Previous studies in this field mostly use the existing models to solve the problem, the steps of which are cumbersome. In this paper, we propose a parking slot detection method that uses directional entrance line regression and classification based on a deep convolutional neural network (DCNN) to make it robust and simple. For parking slots with different shapes and observed from different angles, we represent the parking slot as a directional entrance line. Subsequently, we design a DCNN detector to simultaneously obtain the type, position, length, and direction of the entrance line. After that, the complete parking slot can be easily inferred using the detection results and prior geometric information. To verify our method, we conduct experiments on the public ps2.0 dataset and self-annotated parking slot dataset with 2,135 images. The results show that our method not only outperforms state-of-the-art competitors with a precision rate of 99.68% and a recall rate of 99.41% on the ps2.0 dataset but also performs a satisfying generalization on the self-annotated dataset. Moreover, it achieves a real-time detection speed of 13 ms per frame on Titan Xp. By converting the parking slot into a directional entrance line, the specially designed DCNN detector can quickly and effectively detect various types of parking slots.
Highlights
With the rapid development of artificial intelligence, research on autonomous driving and driver assistance systems has drawn more and more attention from the academy and industry (Chen et al, 2020; Yurtsever et al, 2020)
Ps2.0 dataset (Zhang et al, 2018): the ps2.0 dataset is the largest around-view image dataset with parking slots, including 12,165 around-view images with 600 × 600 pixels corresponding to a ground plane of 10 × 10 m
Our detector has more layers than does the VGG16-based detector, ours achieves the fastest running time, 12 ms. This is because our feature extractor is composed of 3×3 and 1×1 convolution kernels, which means that it has higher computational efficiency. These results show that the specially designed deep convolutional neural network (DCNN) detector is more suitable for directional entrance line detection than the existing networks
Summary
With the rapid development of artificial intelligence, research on autonomous driving and driver assistance systems has drawn more and more attention from the academy and industry (Chen et al, 2020; Yurtsever et al, 2020). It is of great practical meaning to detect parking slots on around-view images via existing cameras on the vehicle. Xu et al (2000) were the first to study vision-based parking slot detection. They detected parking slots based on the fact that the color of the parking slot markings in the image is uniform and is different from the background. This method is affected, as the values of the digital image will change greatly in different lighting scenarios.
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