The Researches Detection Method of Illegal Parking Based on Convolutional Neural Network
Xue-Hong Jiang, Hui-Li Feng, Shi-Yue Li
Available Online 17 February 2020.
- https://doi.org/10.2991/assehr.k.200207.002How to use a DOI?
- convolutional neural networks, city management, image processing, machine vision
- Violation of parking is a legal term. Our country’s law stipulates that there should be no-stop signs and markings, road sections with isolation facilities between motor vehicles and non-motor vehicle lanes and sidewalks, and crosswalks and construction sites. No parking; Railway crossings, sharp bends, narrow roads with widths less than 4 meters, bridges, steep slopes, tunnels, and sections within 50 meters of the above locations are not allowed to stop. In recent years, urban management is gradually developing towards information There are corresponding treatment mechanisms for illegal parking in urban management, but the method of detecting illegal parking based on machine vision is still under study. This paper takes a street in Hebei Province as the research object, and studies the illegal parking under the surveillance image. It also proposes to use the machine vision-based method to automatically detect the illegal parking. In the monitoring image, according to the street view image information detected by the computer, A status assessment of the vehicle placement can be achieved. The detection algorithm in this paper uses the multi-angle suggestion area to accurately locate the offending vehicles in the image and mark them on the detected vehicles. The experimental data shows that the algorithm has good adaptability to the detection of illegal parking in surveillance images, and effectively improves the inspection efficiency.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Xue-Hong Jiang AU - Hui-Li Feng AU - Shi-Yue Li PY - 2020 DA - 2020/02/17 TI - The Researches Detection Method of Illegal Parking Based on Convolutional Neural Network BT - International Academic Conference on Frontiers in Social Sciences and Management Innovation (IAFSM 2019) PB - Atlantis Press SP - 7 EP - 11 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.200207.002 DO - https://doi.org/10.2991/assehr.k.200207.002 ID - Jiang2020 ER -