| dc.contributor.author | Wanzala, J.N | |
| dc.contributor.author | Atim, M.R | |
| dc.date.accessioned | 2022-10-28T09:06:39Z | |
| dc.date.available | 2022-10-28T09:06:39Z | |
| dc.date.issued | 2021 | |
| dc.identifier.citation | Wanzala, J. N., & Atim, M. R. (2021). AI Based Wireless Traffic Light Detector using Image Classification in Machine Learning to Reduce Road Traffic Accidents: A Case Study of Uganda’s Road Traffic. | en_US |
| dc.identifier.uri | http://ir.must.ac.ug/xmlui/handle/123456789/2586 | |
| dc.description.abstract | Road traffic accidents in Uganda have led to many deaths. This unfortunately occurs to pedestrians, users of private and public means of transportations. Many solutions have however focused on for example: sensitization, and building wider roads. This study therefore, focused on using Artificial Intelligence in the traffic control. The results show that image classifier model can detect traffic lights with high accuracy. This implies that, the trained model can be employed in the traffic control system such that traffic control is automated so as to reduce on the driver errors that lead to road traffic accidents. | en_US |
| dc.language.iso | en_US | en_US |
| dc.publisher | International Journal of Electronics Communication and Computer Engineering | en_US |
| dc.subject | Artificial Intelligence (AI) | en_US |
| dc.subject | Image Classification | en_US |
| dc.subject | Traffic Lights | en_US |
| dc.subject | Radio Frequency | en_US |
| dc.subject | Robotics | en_US |
| dc.title | AI Based Wireless Traffic Light Detector using Image Classification in Machine Learning to Reduce Road Traffic Accidents: A Case Study of Uganda’s Road Traffic | en_US |
| dc.type | Article | en_US |
