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"Comprehensive review on vehicle Detection, classification and counting" by Prashan Premaratne, Inas Jawad Kadhim et al.

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 Networks , Computer Vision , Neural Networks , Vehicle Classification , Deep Learning , Vehicle Counting ,

VisualCortex streamlines workflow for computer vision models with Weights & Biases

VisualCortex streamlines workflow for computer vision models with Weights & Biases developer-first MLOps platform ....

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VisualCortex streamlines workflow for computer vision models with Weights & Biases

/PRNewswire/ VisualCortex – the Video Intelligence Platform connecting computer vision s potential to real-world business outcomes – has adopted Weights &. ....

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"Vehicle Detection, Classification and Counting on Highways - Accuracy " by Prashan Premaratne, Rhys Blacklidge et al.

In Australian urban roads, pneumatic tubes are temporarily installed over roads to determine the road usage by vehicles. This is a relatively expensive process and the data cannot be obtained for about two weeks until a manual retrieval of data. In the past, we developed a highly accurate real-time computer vision-based system which relied on back ground subtraction, morphological operations and Gaussian filtering to track centroid of vehicles and accurately determine their speeds and count them. However, in this latest research, we provide our updated system that can determine not only speeds of vehicles but also identifies them including cyclists and pedestrian. This is achieved thorough neural network implementation allowing us to determine their speeds even when they do not follow a straight-line movement. This research utilizes the YOLO family, specifically YOLOv5 for neural network implementation. Such a system is very versatile in determining the variety of traffic in intersecti ....

Computer Vision , Deep Learning , Vehicle Counting , Vehicle Identification ,