Speed Estimation using Ultralytics YOLO11 🚀

Qu'est-ce que l'estimation de la vitesse ?

Speed estimation is the process of calculating the rate of movement of an object within a given context, often employed in computer vision applications. Using Ultralytics YOLO11 you can now calculate the speed of object using object tracking alongside distance and time data, crucial for tasks like traffic and surveillance. The accuracy of speed estimation directly influences the efficiency and reliability of various applications, making it a key component in the advancement of intelligent systems and real-time decision-making processes.

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Avantages de l'estimation de la vitesse ?

• Contrôle efficace du trafic : Une estimation précise de la vitesse permet de gérer le flux de circulation, d'améliorer la sécurité et de réduire les embouteillages sur les routes.
• Navigation autonome précise : Dans les systèmes autonomes comme les voitures auto-conduites, une estimation fiable de la vitesse garantit une navigation sûre et précise du véhicule.
• Amélioration de la sécurité de la surveillance : L'estimation de la vitesse dans l'analyse de la surveillance permet d'identifier les comportements inhabituels ou les menaces potentielles, ce qui améliore l'efficacité des mesures de sécurité.

Applications dans le monde réel

Transport	Transport
Speed Estimation on Road using Ultralytics YOLO11	Speed Estimation on Bridge using Ultralytics YOLO11

import cv2

from ultralytics import solutions

cap = cv2.VideoCapture("Path/to/video/file.mp4")

assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

video_writer = cv2.VideoWriter("speed_management.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

speed_region = [(20, 400), (1080, 404), (1080, 360), (20, 360)]

speed = solutions.SpeedEstimator(model="yolo11n.pt", region=speed_region, show=True)

while cap.isOpened():
    success, im0 = cap.read()

    if success:
        out = speed.estimate_speed(im0)
        video_writer.write(im0)
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
        continue

    print("Video frame is empty or video processing has been successfully completed.")
    break

cap.release()
cv2.destroyAllWindows()

Arguments SpeedEstimator

Arguments model.track

FAQ

How do I estimate object speed using Ultralytics YOLO11?

Estimating object speed with Ultralytics YOLO11 involves combining object detection and tracking techniques. First, you need to detect objects in each frame using the YOLO11 model. Then, track these objects across frames to calculate their movement over time. Finally, use the distance traveled by the object between frames and the frame rate to estimate its speed.

Exemple:

import cv2

from ultralytics import solutions

cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
video_writer = cv2.VideoWriter("speed_estimation.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Initialize SpeedEstimator
speed_obj = solutions.SpeedEstimator(
    region=[(0, 360), (1280, 360)],
    model="yolo11n.pt",
    show=True,
)

while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        break
    im0 = speed_obj.estimate_speed(im0)
    video_writer.write(im0)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

Pour plus de détails, reporte-toi à notre article de blog officiel.

What are the benefits of using Ultralytics YOLO11 for speed estimation in traffic management?

Using Ultralytics YOLO11 for speed estimation offers significant advantages in traffic management:

• Sécurité renforcée: Estime avec précision la vitesse des véhicules pour détecter les excès de vitesse et améliorer la sécurité routière.
• Real-Time Monitoring: Benefit from YOLO11's real-time object detection capability to monitor traffic flow and congestion effectively.
• Évolutivité: Déploie le modèle sur diverses configurations matérielles, des appareils périphériques aux serveurs, ce qui garantit des solutions flexibles et évolutives pour les mises en œuvre à grande échelle.

Pour plus d'applications, voir les avantages de l'estimation de la vitesse.

Can YOLO11 be integrated with other AI frameworks like TensorFlow or PyTorch?

Yes, YOLO11 can be integrated with other AI frameworks like TensorFlow and PyTorch. Ultralytics provides support for exporting YOLO11 models to various formats like ONNX, TensorRT, and CoreML, ensuring smooth interoperability with other ML frameworks.

To export a YOLO11 model to ONNX format:

yolo export --weights yolo11n.pt --include onnx

Pour en savoir plus sur l'exportation des modèles, consulte notre guide sur l'exportation.

How accurate is the speed estimation using Ultralytics YOLO11?

The accuracy of speed estimation using Ultralytics YOLO11 depends on several factors, including the quality of the object tracking, the resolution and frame rate of the video, and environmental variables. While the speed estimator provides reliable estimates, it may not be 100% accurate due to variances in frame processing speed and object occlusion.

Remarque: il faut toujours tenir compte de la marge d'erreur et valider les estimations avec les données de la vérité terrain lorsque c'est possible.

Pour d'autres conseils sur l'amélioration de la précision, consulte la rubrique Arguments SpeedEstimator section.

Why choose Ultralytics YOLO11 over other object detection models like TensorFlow Object Detection API?

Ultralytics YOLO11 offers several advantages over other object detection models, such as the TensorFlow Object Detection API:

• Real-Time Performance: YOLO11 is optimized for real-time detection, providing high speed and accuracy.
• Ease of Use: Designed with a user-friendly interface, YOLO11 simplifies model training and deployment.
• Polyvalence: Prend en charge de multiples tâches, notamment la détection d'objets, la segmentation et l'estimation de la pose.
• Community and Support: YOLO11 is backed by an active community and extensive documentation, ensuring developers have the resources they need.

For more information on the benefits of YOLO11, explore our detailed model page.

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