|
Blind-Spot Monitoring Using Deep Learning
|
|
| Author:
|
T. PONNARASAN, M.G. SUMITHRA, I. RUBAN, T.R. RANJITH KUMAR
|
| Abstract:
|
In recent years, the rapidly increasing vehicular population, driving environment and human factors has led to a lot of traffic accidents. If there exists a mechanism to detect obstructions in the road, and then relay the processed information back to the driver. He may be alerted about the impending danger. This paper proposes a vision-based monitoring algorithm to detect vehicles in a blind-spot area using three rear-view cameras. Three frames are stitched to obtain a single wide-range frame. The stitched frame eliminates a major portion of the vehicle’s blind-spot. This system uses the YOLO algorithm to detect a vehicle in the detection window. The alarm signal is generated if the detected vehicle in the blind-spot crosses the hazardous zone. Vehicle data set with truck, bus, car, and motor-cycle is used to train the YOLO model. The processing speed of the system is improved while keeping performance degradation as minimal as possible.
|
| Keyword:
|
Blind-spot Monitoring, Video Stitching, YOLO.
|
| EOI:
|
-
|
| DOI:
|
https://doi.org/10.31838/ijpr/2020.12.01.232
|
| Download:
|
Request For Article
|
|
|