Sensor-based Crosswalk road driving monitoring
name | role |
---|---|
Lee Heerae(Team Leader) | DeepLearing Model Develop |
Kim Seongyeong | Hardware controll |
Park Eunbi | Server & Front Develop |
- In accordance with eco-friendly means of vehicle and distancing due to COVID-19
- In 2021, the number of e-scooter users increased more than 5% compared to 2019
traffic accidents also has been increased 2.5%
- To ensure the safety of occupants and pedestrians and the safe operation of nearby vehicles
It is necessary to crack down on compliance with electric kickboard traffic laws - Therefore, we will create a system to monitor 'electric driving crossing of a crosswalk', which had the highest violation rate
Source: Report on the results of a survey on the safety status of electric kickboard sharing service
- Using the webcam on the front of the scooter, verify that the scooter user passes the crosswalk
- Using a accelerometer sensor and switch sensor on the scooter footrest, it is determined whether the scooter user is driving or not
(Absence of pressure sensor replaced by a switch sensor) - Using the buzzer sensor inside the scooter, scooter users are warned when driving at the crosswalk
- Using GPS sensor on the front of the scooter, when a violation is determined user id, location and time infomation are send to the database
- When the Raspberry Pi detects driving in a crosswalk, it transmits violation information consisting of the occupant ID, its longitude and latitude, and the time of violation to the database
- Violation images taken by webcam are sent to the s3 file server
- Using mysql and matplotlib, we analyze the violation status by time period, violation status by date, and users with many violations to create a graph
- Based on the data analysis result, the violation information, and the violation image, a web page was constructed, and the web server was configured using the flask server
- Auto tracking system helps to detect driving violations
- Resolving the problem of shortage of enforcement personnel
- Comprehensive investigation of safety management and driving conditions
- Improved e-scooter users' awareness of safe driving
- Pedestrian safety protection
- Increased use of e-scooter using electrical energy help to be green
You must download Yolov5
After downloading, you need to move crosswalk_model.py into the YOLOv5 folder.
Ada/crosswalk_hardware/main_process.py : detect driving across a crosswalk on an e-scooter
python3 main_process.py
Ada/crosswalk_model/yolov5/crosswalk_model.py : Crosswalk detected model main file
python3 crosswalk_model.py
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