Fala: An intelligent mobile application for safe driving

Kenechukwu Sylvanus Anigbogu *, Tochukwu Sunday Belonwu, Okwuchukwu Ejike Chukwuogo, Emmanuel Chibogu Asogwa and Joshua Makuo Nwankpa

Department of Computer Sciences, Nnamdi Azikiwe University Awka, Anambra State, Nigeria.
 
Research Article
International Journal of Science and Research Archive, 2023, 10(01), 180–186.
Article DOI: 10.30574/ijsra.2023.10.1.0729
Publication history: 
Received on 30 July 2023; revised on 06 September 2023; accepted on 08 September 2023
 
Abstract: 
Studies have shown that researchers have proposed various applications for Safe driving using different driving activities and events. This research considered pothole as one of the challenging factors for safe driving therefore it looked into deploying a mobile application for monitoring potholes as an important tool for safe driving. Automating pothole detection will go a long way in providing safe driving for road users and intelligent transportation systems. This paper presents a safe driving application that assists drivers by detecting and predicting potholes while on the road to curb road accidents in Nigeria. The datasets used in this research were potholes images extracted from kaggle which were classified into two; potholes and normal roads. The object detection algorithm that was used to evaluate the model is YOLOv5. The results proved that our model was not perverse. We deployed the model to the mobile application, the mobile application when launched activates the camera by default enabling the system to detect and predict between normal roads and potholes. The predicted values were all positive. The two classifiers were all detected perfectly in real-time while testing without being perverse. The system presents its predicted value in percentage, therefore showing the level of adherence to each of the classes detected.
 
Keywords: 
Safe driving; Object detection; YOLO 5; Pothhole detection; Mobile application
 
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0