Intelligent Traffic Surveillance for Real-Time Vehicle Detection, License Plate Recognition, and Speed Estimation
It addresses critical traffic management and safety needs in resource-constrained environments, offering an automated enforcement solution to potentially reduce road accidents in developing countries.
The study tackled real-time traffic surveillance for vehicle detection, license plate recognition, and speed estimation in developing countries like Uganda, achieving a 97.9% mAP for license plate detection, a 1.79% CER for character recognition, and a 10 km/h margin of error for speed estimation.
Speeding is a major contributor to road fatalities, particularly in developing countries such as Uganda, where road safety infrastructure is limited. This study proposes a real-time intelligent traffic surveillance system tailored to such regions, using computer vision techniques to address vehicle detection, license plate recognition, and speed estimation. The study collected a rich dataset using a speed gun, a Canon Camera, and a mobile phone to train the models. License plate detection using YOLOv8 achieved a mean average precision (mAP) of 97.9%. For character recognition of the detected license plate, the CNN model got a character error rate (CER) of 3.85%, while the transformer model significantly reduced the CER to 1.79%. Speed estimation used source and target regions of interest, yielding a good performance of 10 km/h margin of error. Additionally, a database was established to correlate user information with vehicle detection data, enabling automated ticket issuance via SMS via Africa's Talking API. This system addresses critical traffic management needs in resource-constrained environments and shows potential to reduce road accidents through automated traffic enforcement in developing countries where such interventions are urgently needed.