This repository offers a comprehensive solution for vehicle detection and recognition in images and videos. Leveraging state-of-the-art computer vision algorithms and deep learning techniques, the project aims to provide accurate and efficient detection and recognition of vehicles for various applications including traffic management, surveillance, and autonomous driving.
-
Robust Detection: Utilizes advanced object detection algorithms such as YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector) to robustly detect vehicles in diverse environmental conditions.
-
Recognition Capabilities: Implements deep learning models for vehicle attribute recognition, including vehicle type, make, model, and license plate identification.
-
Real-time Performance: Optimized for real-time processing, enabling rapid detection and recognition of vehicles in streaming video feeds.
-
Customizable Architecture: Offers a modular architecture that allows users to easily customize and integrate detection and recognition models based on specific requirements and datasets.
-
Evaluation Metrics: Includes evaluation scripts to assess the accuracy and performance of the detection and recognition models using standard evaluation metrics such as precision, recall, and F1 score.
Contributions from the community are welcome! Feel free to open issues, suggest enhancements, or submit pull requests to improve the project.