Visual Detection and Image Processing of Parking Space Based on Deep Learning

Abstract

The automatic parking system based on vision is greatly affected by uneven lighting, which is difficult to make an accurate judgment on parking spaces in the case of complex image information. To solve this problem, this paper proposes a parking space visual detection and image processing method based on deep learning. Firstly, a 360-degree panoramic system was designed to photograph the vehicle environment. The image has been processed to obtain a panoramic aerial view, which was input as the original image of the parking space detection system. Secondly, the Faster R-CNN (Region-Convolutional Neural Network) parking detection model was established based on deep learning. It was aimed to detect and extract the parking space from the input image. Thirdly, the problems of uneven illumination and complex background were solved effectively by removing the background light from the image. Finally, a parking space extraction method based on the connected region has been designed, which further simplified the parking space extraction and image processing. The experiment results show that the mAP (mean Average Precision) value of the Faster R-CNN model using 101-Floor ResNet as the feature extraction network is 89.30%, which is 2.28% higher than that of the Faster R-CNN model using 50-Floor ResNet as the feature extraction network. The model built in this paper can detect most parking spaces well. The position of the output target box is accurate. In some test scenarios, the confidence of parking space recognition can even reach 100%. In summary, the proposed method can realize the effective identification and accurate positioning of parking spaces.

Review of Vision-Based Deep Learning Parking Slot Detection on Surround View Images

“…Huang et al [ 12 ] used Faster R-CNN and image processing techniques for parking slot detection. This is a mixed approach of object detection and traditional computer vision techniques.…”

“…To achieve our goal, we will categorize the parking slot detection techniques into several distinct categories. These categories include Object Detection-Based approaches [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ], which employ either Single-stage Detection or Multi-stage Detection techniques to locate the parking slot in the image. We will also explore Image Segmentation-Based techniques [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ], which involve segmenting the parking slots lines from the background to achieve accurate detection.…”

“…These works will serve as the foundation for our comprehensive review of vision-based deep learning parking slot detection on surround view images. [6] 2020 IEEE Li et al [7] 2020 MDPI Wu et al [8] 2020 arXiv Xu and Hu [9] 2020 IOP Chen et al [10] 2021 Springer Xie and Wei [11] 2021 IEEE Huang et al [12] 2022 MDPI Suhr and Jung [13] 2022 IEEE Wang et al [14] 2022 IEEE Zheng et al [15] 2022 IEEE Bui and Suhr [16] 2023 IEEE Lee et al [17] 2023 IEEE Bui and Suhr [18] 2023 IEEE Wu et al [19] 2018 IEEE…”

Rethinking Feature Generalization in Vacant Space Detection

“…They are typically installed above each parking space and can detect changes in the infrared light caused by the presence of a vehicle. Video cameras [ 6 , 7 , 8 , 9 , 10 , 11 ] can be used to detect the presence or absence of a vehicle in a parking space. They are typically installed above each parking space and use image analysis software to detect changes in the video feed caused by the presence of a vehicle.…”

Research on Parking Space Status Recognition Method Based on Computer Vision

“…Li Yongyi et al [21] proposed to improve artificial potential field method to complete automatic driving trajectory planning, and introduced invasive weed algorithm to solve the defects of traditional artificial potential field method. Huang C et al [22] designed a parking extraction method based on the connected region, which further simplified parking extraction and image processing. Fintzel et al [23] and Vestri et al [24] identified parking spaces from the perspective of stereo ranging; Li Yongyi.…”