Vehicle and Pedestrian Detection Using YOLO-NAS and Kitti dataset

Vehicle and pedestrian detection is a crucial task in the field of computer vision and autonomous driving. YOLO-NAS (You Only Look Once Neural Architecture Search) is a state-of-the-art object detection model that achieves high accuracy and efficiency in real-time detection tasks.

The Kitti dataset is a widely used benchmark dataset for object detection and tracking in autonomous driving scenarios. It contains images and video sequences captured from a moving vehicle with annotations for various objects, including vehicles and pedestrians.

Methodology

In this project, we trained the YOLO-NAS model on the Kitti dataset to detect vehicles and pedestrians in real-time. The YOLO-NAS model is a one-stage object detection model that directly predicts bounding boxes and class labels for multiple objects in a single pass.

We preprocessed the Kitti dataset by resizing and augmenting the images to improve the robustness of the model. We then fine-tuned the YOLO-NAS model on the preprocessed dataset to improve its performance on vehicle and pedestrian detection tasks.

Results

The trained YOLO-NAS model achieved high accuracy in detecting vehicles and pedestrians in the Kitti dataset. The model was able to accurately localize and classify objects in real-time, making it suitable for autonomous driving applications.

By combining the power of YOLO-NAS and the Kitti dataset, we were able to build a robust vehicle and pedestrian detection system that can be deployed in autonomous vehicles for safe and efficient navigation.

Conclusion

Vehicle and pedestrian detection using YOLO-NAS and the Kitti dataset is a promising approach for enhancing the capabilities of autonomous driving systems. The high accuracy and efficiency of the YOLO-NAS model, combined with the rich and diverse annotations in the Kitti dataset, make it a powerful combination for real-time object detection tasks.

Further research and development in this area can lead to the deployment of more reliable and safe autonomous driving systems that can navigate complex environments with ease and precision.