Browsing by Author "Datta, Anik"

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    An Intelligent in Vehicle Smart Driving Aid on Real-world Driver Performance
    (Daffodil International University, 2020-07-12) Datta, Anik; Shuvo, Shakilur Rahman; Bhuiya, Mehedi Hasan; Halder, Prithweeraj
    In the present world, most of the cities have intelligent transport and traffic systems. The current trend of drivers violating traffic laws and driving at high speeds can be noticed due to which road accidents are constantly happening and cars and people are being harmed.. This proposed system helps the driver to obey traffic rules and regulations. Today most of the automobiles have an on board automotive driver assistance gadget aiming to alert drivers about riding environments. In this report we want to talk about this kind of system, when the driver drives the vehicle, this system detects the road side sign and also detects humans, other vehicles etc. Depending on detection, it passes through the notification to the driver. If suddenly the vehicle is in an accident then this device through the notification of the nearest hospital and police station depends on location tracking technology.
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    Road Object Detection in Bangladesh Using Faster R-CNN
    (Scopus, 2020) Datta, Anik; Meghla, Tamara Islam; Khatun, Tania; Bhuiya, Mehedi Hasan; Shuvo, Shakilur Rahman; Rahman, Md. Mahfujur
    The importance of object detection in our lives is increasing day by day. The role of object detection is very important in autonomous cars, intelligent driving assistance, and advanced traffic analysis. In the case of traffic analysis and intelligent driving assistance in Bangladesh, it is very important to properly identify all the objects from real-time video. Because in both cases the main responsibility of the system is to give the driver or authority a clear idea about the road or the environment around the vehicle. And for this, we need to use modern algorithms and architecture based neural network models with much better object detection accuracy such as Faster R-CNN. There are currently a couple of algorithms that work faster than Faster R-CNN but cannot detect objects accurately, as is the case with small-to-medium objects. We used Faster R-CNN on our data to analyze the environment around the road and the environment around the car. We trained the network for 19 object classes and tested its ability to detect objects with real-time video analysis with an accuracy of 86.42%. Moreover, FPR(false positive rate) and FNR(false negative rate) is calculated to evaluate the proposed model from confusion matrices. In this study, the FPR of the Faster R-CNN model is 15.97% and the FNR of the Faster R-CNN model is 12.2%