Client
Challenge
In urban environments, monitoring and enforcing traffic regulations is a critical yet challenging task. Traditional methods of traffic surveillance often require substantial human involvement and are prone to errors. This project aimed to automate the process of detecting traffic violations and issuing citations, reducing the need for manual intervention and increasing the accuracy and efficiency of traffic law enforcement.
Goal
The primary objective was to develop a robust system capable of detecting various traffic violations in real-time using advanced machine learning techniques. The system needed to accurately identify violations, recognize vehicle license plates, and generate automated citations without human involvement.
Result
1. Technology Stack: The project utilized Python, Deep Neural Networks (DNN), YOLOv5 for object detection, and EasyOCR for Optical Character Recognition (OCR). This combination ensured high accuracy in both violation detection and license plate recognition. 2. Data Collection and Preparation: A comprehensive dataset was created by aggregating data from six existing datasets, resulting in a total of 31,000 images. This extensive dataset was essential for training the models to recognize a wide variety of traffic violations. 3. Model Training and Testing: Multiple YOLOv5 models were trained on this dataset to detect different types of violations such as speeding, illegal parking, and signal jumping. The models were rigorously tested to ensure they could perform well in diverse real-world scenarios. 4. License Plate Recognition: EasyOCR was integrated into the system to extract license plate information from the detected vehicles. This step was crucial for generating accurate citations. 5. Automation: The entire process was automated to function in real-time. The system was designed to continuously monitor live video feeds from traffic cameras, detect violations, recognize license plates, and issue citations seamlessly. Results: Accuracy and Efficiency: The system demonstrated high accuracy in detecting traffic violations and recognizing license plates. The automation significantly reduced the need for human oversight, thus increasing the efficiency of traffic enforcement operations. Scalability: The project was designed to be scalable, capable of being deployed across multiple urban locations with minimal modifications. Publication: The innovative approach and successful implementation of this project have been documented in a paper currently under review by Wiley Publications, highlighting its potential impact on traffic management systems globally. This project not only showcased advanced technical skills in machine learning and computer vision but also underscored the potential for AI to transform traditional processes in urban management. The success of the Vehicle Violation Detection System serves as a testament to the power of technology in enhancing public safety and operational efficiency.