Vehicle and pedestrian detection:
Github: [ Ссылка ]
For queries: You can comment in comment section or you can mail me at aarohisingla1987@gmail.com
Why vehicle and Pedestrian detection is important?
Vehicle and pedestrian detection plays a crucial role in the development of autonomous vehicles and smart city applications, serving as a foundation for safety and efficiency. In the context of autonomous vehicles, accurate detection ensures that vehicles can navigate complex urban environments safely, recognizing and reacting to pedestrians, cyclists, and other vehicles in real-time to prevent accidents. This capability is fundamental for the vehicles to make informed decisions about speed, direction, and when to stop or yield, enhancing road safety for all users. In smart cities, vehicle and pedestrian detection contributes to the optimization of traffic flow and the management of public spaces. By analyzing movement patterns, smart city systems can adjust traffic signals to reduce congestion, improve pedestrian crossing times, and even allocate resources more efficiently, leading to safer, more sustainable, and user-friendly urban environments. Together, these technologies pave the way for a future where mobility is more integrated, predictive, and adaptive to the needs of society.
What is YOLO-NAS?
YOLO-NAS is an Object Detection foundational model generated by Deci’s AutoNAC™ engine based on Neural Architecture Search Technology.
The model provides superior real-time object detection capabilities and production-ready performance.
YOLO-NAS delivers state-of-the-art performance with the unparalleled accuracy- speed performance, outperforming other models such as YOLOv5, YOLOv6, YOLOv7 and YOLOv8.
Deci's website for more detailed information on YOLO-NAS and the YOLO-NAS GitHub repository:
○ YOLO-NAS Technical Blog: [ Ссылка ]
○ YOLO-NAS Github repo: [ Ссылка ]
○ SuperGradients Documentation: [ Ссылка ]
○ Deci’s Models: [ Ссылка ]
Kitti dataset:
KITTI dataset, is a popular dataset used for computer vision tasks, particularly in the context of autonomous driving.
KITTI includes detailed annotations for vehicles, pedestrians, cyclists, and other objects in 3D and 2D formats. These annotations are manually verified, ensuring high accuracy and consistency, which is crucial for training and evaluating detection models.
The KITTI dataset encompasses a variety of data types, including stereo images, optical flow, LiDAR point clouds, and GPS positions. Stereo images captured from cameras mounted on a vehicle. These images are used for tasks such as 2D object detection (where objects are identified and localized using bounding boxes on the images), optical flow (to measure the motion between two frames), LiDAR (Light Detection and Ranging) point clouds, which provide detailed 3D representations of the vehicle's surroundings.
The KITTI dataset's integration of both 2D and 3D data allows for a wide range of applications, from basic image processing to advanced 3D mapping and environment perception, making it a versatile tool for developing and benchmarking autonomous vehicle technologies.
#computervision #autonomousvehicles #objectdetection #yolo #yolov7 #yolov8 #yolonas
