- Built by Dong Chen, Zhaojian Li from Michigan State University
- Started on Oct.19, 2019, Lastly updated on Sept.14, 2021
This project aims at building a on-device APP used to asist human drivers. This APP combines three basic functions: object detection(vehicle, traffic light, stop sign, pedestrain), lane deviation warning and distance estimation.
To be added...
We use the deep learning methods to do object detection. To be specific, we use the YOLO-v3 model to do object detection, here we are only curious about traffic-related objects, such as vehicles, pedestrain, traffic lights and stop signs.
Considered limited computing resources on mobile devices (smart phones), we adapt the convential computer vision methods.
Modification logs:
- Delete the display code for "intermediate pipeline images".
- Simiplify codes.
- Problems with road curvature and offset values are always positive.
47o FOV len.
When camera pitch angle is negligibly small, range d to vehicle can be calculated as in the following: d = F_c * H_c / (y_b - y_h)
For privacy issues, there are few open resources for dash camera videos. We will show our application by three different video demos.
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Stop sign detection
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Traffic light detection
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Lane deviation detection
- YOLOv3: An Incremental Improvement
- What’s new in YOLO v3?
- Integration of Vehicle Detection and Distance Estimation using Stereo Vision for Real-Time AEB System
- Robust Range Estimation with a Monocular Camera for Vision-Based Forward Collision Warning System
- Advanced Lane Finding
- Lane Departure Warning System for Autonomous Driving
To be added...