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

Abstract

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 DCNN (Deep Convolutional Neural Networks) 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 markingpoints. 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 largescale 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/.

Around View Monitoring-Based Vacant Parking Space Detection and Analysis

“…They adopted boosting decision trees to train a marking-point detector to infer the parking space. In addition, Zhang et al [19] proposed a deep convolutional neural network (DCNN) to formulate the parking space detection problem as two sub-problems-local image pattern classification and marking-point detection-to efficiently detect parking spaces.…”

Smartphone-Based Context Flow Recognition for Outdoor Parking System with Machine Learning Approaches

“…It examines the pictures taken by the cameras to identify and discover the unfilled parking slot. Reference [16] proposed a vision-based algorithm, using a deep convolutional neural network that took consideration of surrounding images as the input. A wide-angle camera was utilized to cover the whole parking area in the proposed method by [17], in which anyone without any technical background could setup the system easily.…”

Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

“…In the occupancy classification process, a customized DCNN is designed to make the parking slot occupancy classification reliable. Finally, VPS-Net is evaluated in the ps2.0 dataset [11] and PSV dataset [24]. The results show that VPS-Net outperforms previous methods with a precision rate of 99.63% and a recall rate of 99.31%.…”

“…As the most important component of the PAS, the task of the object position designation is to detect a vacant parking slot accurately. The PAS can be divided into four categories based on the parking space detection method: free space-based [3][4][5][6][7], parking slot marking-based [8][9][10][11], interface-based [12][13][14], and infrastructure-based [15][16][17]. Compared with other methods, 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.…”

“…These methods utilize designed features to detect marking points, which are easily affected by lighting variations. To detect marking points robustly, a method utilizing the deep convolutional neural network (DCNN) was proposed in [11]. Due to the powerful feature extraction ability of DCNN, this method significantly improves the accuracy of parking slot detection.…”