Enhancing Front-Vehicle Detection in Large Vehicle Fleet Management

Transportation safety has been widely discussed for avoiding forward collisions. The broad concept of remote sensing can be applied to detect the front of vehicles without contact. The traditional Haar features use adjacent rectangular areas for many ordinary vehicle studies to detect the front vehi...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-04, Vol.14 (7), p.1544
Main Authors: Mu, Ching-Yun, Kung, Pin, Chen, Chien-Fu, Chuang, Shu-Cheng
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Automobile safety
Cameras
Carbon
Collisions
Deep learning
Digital television
Digital video recorders
Environmental impact
fence method
Fleet management
Global positioning systems
GPS
Haar features
Infrared cameras
Machine learning
Methods
near-infrared camera
Remote sensing
Roads & highways
Safety management
Sensors
Traffic accidents & safety
Traffic congestion
Transportation planning
Transportation safety
Vehicles
YOLOv4
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Summary:Transportation safety has been widely discussed for avoiding forward collisions. The broad concept of remote sensing can be applied to detect the front of vehicles without contact. The traditional Haar features use adjacent rectangular areas for many ordinary vehicle studies to detect the front vehicle images in practice. This paper focused on large vehicles using a front-installed digital video recorder (DVR) with a near-infrared (NIR) camera. The views of large and ordinary vehicles are different; thus, this study used a deep learning method to process progressive improvement in moving vehicle detection. This study proposed a You Only Look Once version 4 (YOLOv4) supplemented with the fence method, called YOLOv4(III), to enhance vehicle detection. This method had high detection accuracy and low false omission rates using the general DVR equipment, and it provided comparison results. There was no need to have a high specification front camera, and the proposed YOLOv4(III) was found to have competitive performance. YOLOv4(III) reduced false detection rates and had a more stable frame per second (FPS) performance than with Haar features. This improved detection method can give an alert for large vehicle drivers to avoid serious collisions, leading to a reduction in the waste of social resources.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14071544