Parking lot

Convolutional neural network

Engineering

Artificial intelligence

Pattern recognition (psychology)

Pure mathematics

Mathematics

Image (mathematics)

Geography

Civil engineering

Computer science

Cartography

Geodesy

Benchmark (surveying)

Scale (ratio)

Field (mathematics)

Key (lock)

Computer vision

Computer security

IEEE Transactions on Image Processing

In the automobile industry, recent years have witnessed a growing interest in developing self-parking systems. For such systems, how to accurately and efficiently detect and localize the parking slots defined by regular line segments near the vehicle is a key and still unresolved issue. In fact, kinds of unfavorable factors, such as the diversity of ground materials, changes in illumination conditions, and unpredictable shadows caused by nearby trees, make the vision-based parking-slot detection much harder than it looks. In this paper, we attempt to solve this issue to some extent and our contributions are twofold. First, we propose a novel deep convolutional neural network (DCNN)-based parking-slot detection approach, namely, DeepPS, which takes the surround-view image as the input. There are two key steps in DeepPS, identifying all the marking points on the input image and classifying local image patterns formed by pairs of marking points. We formulate both of them as learning problems, which can be solved naturally by modern DCNN models. Second, to facilitate the study of vision-based parking-slot detection, a large-scale labeled dataset is established. This dataset is the largest in this field, comprising 12 165 surround-view images collected from typical indoor and outdoor parking sites. For each image, the marking points and parking slots are carefully labeled. The efficacy and efficiency of DeepPS have been corroborated on our collected dataset. To make our results fully reproducible, all the relevant source codes and the dataset have been made publicly available at https://cslinzhang.github.io/deepps/.

Vision-Based Parking-Slot Detection: A DCNN-Based Approach and a Large-Scale Benchmark Dataset