Speed Estimation using Ultralytics YOLO11 🚀

속도 추정이란 무엇인가요?

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.

Watch: Speed Estimation using Ultralytics YOLO11

속도 예측의 장점은?

• 효율적인 교통 통제: 정확한 속도 예측은 교통 흐름을 관리하고 안전을 강화하며 도로의 혼잡을 줄이는 데 도움이 됩니다.
• 정밀한 자율주행 내비게이션: 자율주행차와 같은 자율 주행 시스템에서 신뢰할 수 있는 속도 예측은 안전하고 정확한 차량 내비게이션을 보장합니다.
• 강화된 보안 감시: 감시 분석의 속도 추정 기능은 비정상적인 행동이나 잠재적 위협을 식별하여 보안 조치의 효과를 개선하는 데 도움이 됩니다.

실제 애플리케이션

교통편	교통편
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()

인수 SpeedEstimator

인수 model.track

자주 묻는 질문

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.

예시:

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()

자세한 내용은 공식 블로그 게시물을 참조하세요.

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:

• 향상된 안전성: 차량 속도를 정확하게 예측하여 과속을 감지하고 도로 안전을 개선합니다.
• Real-Time Monitoring: Benefit from YOLO11's real-time object detection capability to monitor traffic flow and congestion effectively.
• 확장성: 엣지 디바이스에서 서버에 이르기까지 다양한 하드웨어 설정에 모델을 배포하여 대규모 구현을 위한 유연하고 확장 가능한 솔루션을 보장합니다.

더 많은 애플리케이션에 대한 자세한 내용은 속도 추정의 장점을 참조하세요.

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

내보내기 가이드에서 모델 내보내기에 대해 자세히 알아보세요.

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.

참고: 항상 오차 범위를 고려하고 가능하면 실측 데이터로 추정치를 검증하세요.

추가 정확도 향상 팁을 확인하려면 인수 SpeedEstimator 섹션.

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.
• 다목적성: 물체 감지, 세분화, 포즈 추정 등 다양한 작업을 지원합니다.
• 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.

9개월 전 생성됨 ✏️ 2 일 전 업데이트됨

댓글