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Cloud-based obstacle detection system for drivers

Eio, Hua Zen (2020) Cloud-based obstacle detection system for drivers. Final Year Project, UTAR.

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    Abstract

    Based on the past statistics and record, majority of the road accidents take place because driver is not concentrated enough in driving and causing lack of response time to instant traffic events. People expect to have an automated system that provides drivers the traffic sign information and detect the road condition. One of the most important functions is obstacle detection and recognition. This system involves the use of camera to capture the real-time road condition then identify the obstacle which are encountered by the vehicle, then provides correct information to the user. In this paper, the project proposed is cloud-based obstacle detection system for drivers. It is one of the most popular example of artificial intelligence system that used to detect obstacle. Artificial intelligence (AI) is something intelligent and it could perform things that only human can perform. It might even be more powerful than the human if it was well trained and developed. The system will be developed in mobile application. The application will provide information of the road condition to user once the obstacle is detected. The detected obstacle will be uploaded to database server whereby other user is able to access the information as well. To enable the mobile application to be more user-friendly, the information of detected object will be displayed in the form of icon on the map. User can simply click on the icon to know more details about the detected objects. For example, the date and time of detection, the name of user upload the data, the name of object detected, the actual location of the object and so on. The application will be developed with the help of Android Studio, Google Maps JavaScript API and TensorFlow API. The system can only to be operated when accessing to Internet. To study the performance of this cloud-based obstacle detection system, several evaluations were conducted.

    Item Type: Final Year Project / Dissertation / Thesis (Final Year Project)
    Subjects: T Technology > T Technology (General)
    T Technology > TA Engineering (General). Civil engineering (General)
    Divisions: Faculty of Information and Communication Technology > Bachelor of Information Systems (Hons) Information Systems Engineering
    Depositing User: ML Main Library
    Date Deposited: 05 Jan 2021 16:46
    Last Modified: 05 Jan 2021 16:46
    URI: http://eprints.utar.edu.my/id/eprint/3788

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