The Researches Detection Method of Illegal Parking Based on Convolutional Neural Network

DOI

10.2991/assehr.k.200207.002How to use a DOI?

Keywords

convolutional neural networks, city management, image processing, machine vision

Abstract

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.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 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  - 10.2991/assehr.k.200207.002
ID  - Jiang2020
ER  -