Comprehensive review on vehicle Detection, classification and counting on highways
Publication Name
Neurocomputing
Abstract
Vehicle detection, counting and finally classification has been an important aspect of traffic analysis specially on highways in many developed and developing nations. This has vitalized the monitoring of freeways and reduced the reliance on human traffic monitors specially in developed nations. However, much research carried out in this regard since 1995 have been slow to progress until around 2000. Since then, much more encouraging outcomes have been achieved. This has been mainly due to the advances on vision-based computing and the miniaturizing of hardware since the early 2000. Initial vision-based systems used basic computer vision approaches such as background subtraction and edge detection to detect and count vehicles. Early progress of classification had little success until around 2010. Since 2010, many computer vision approaches using Neural Networks have been gradually increasing the efficiency and the realtime operation of such systems. Since then, many deep learning based neural network approaches have produced remarkable outcomes specially in classification. We have also reported highly accurate and efficient deep learning based YOLOv5 system that has recorded an astounding success. We have used models YOLOv5l, YOLOv5m, YOLOv5n and YOLOv5s in our research to ascertain the best model for the task. However, low light conditions associated with overcast, and dusk have significantly reduced the accuracy in many deep learning-based systems. There are other techniques based on approaches such as headlight detection have appeared to increase the accuracy of counting however, classification at night without adequate lighting still pauses a formidable challenge. The work presented here analyses the vehicle counting problem using computer vision providing many different viewpoints in a view to provide the reader with accurate problem definition.
Open Access Status
This publication may be available as open access
Volume
556
Article Number
126627