Road users detection for traffic congestion classification
نویسندگان
چکیده
One of the important problems that urban residents suffer from is Traffic Congestion. It makes their life more stressful, it impacts several sides including economy: by wasting time, fuel and productivity. Moreover, psychological physical health. That road authorities required to find solutions for reducing traffic congestion guaranteeing security safety on roads. To this end, detecting users in real-time allows providing features information about specific points. These last are useful managers also congested The goal build a model detect vehicles pedestrians using artificial intelligence especially machine learning computer vision technologies. This paper provides an approach as input dataset 22983 images, each image contains than one target objects, generally 81000 distributed persons (pedestrians), cars, trucks/buses (vehicles), motorcycles/bicycles. used study known Common Objects Context (MS COCO) published Microsoft. Furthermore, six different models were built based approaches RCNN, Fast Faster Mask 5th 7th versions YOLO. In addition, comparison these evaluation metrics was provided. As result, chosen able with 55% terms mean average precision.
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ژورنال
عنوان ژورنال: Mathematical modeling and computing
سال: 2023
ISSN: ['2312-9794', '2415-3788']
DOI: https://doi.org/10.23939/mmc2023.02.518