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8-Object Detection on Jetson (Ultralytics YOLO)

This section demonstrates object detection on NVIDIA Jetson using Ultralytics YOLO. It covers detection on images, videos, and real-time camera streams with Jetson performance optimization.

1. Enable Optimal Jetson Performance​

Before running inference, configure the Jetson board for maximum performance.

Enable MAX Power Mode​

sudo nvpmodel -m 2

Enable Jetson Clocks​

sudo jetson_clocks

2. Object Detection on Images​

Enter Demo Directory​

cd ~/ultralytics/ultralytics/yahboom_demo

Run Image Detection Script​

python3 01.detection_image.py

Detection results are saved to:

~/ultralytics/ultralytics/output/

Sample Code (Image Detection)​

from ultralytics import YOLO

model = YOLO("yolo11n.pt")
results = model("assets/bus.jpg")

for r in results:
    r.show()
    r.save(filename="output/bus_output.jpg")

3. Object Detection on Videos​

Run Video Detection Script​

python3 01.detection_video.py

Output video location:

~/ultralytics/ultralytics/output/

Sample Code (Video Detection)​

import cv2
from ultralytics import YOLO

model = YOLO("yolo11n.pt")
cap = cv2.VideoCapture("videos/people_animals.mp4")

width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))

out = cv2.VideoWriter(
    "output/people_animals_output.mp4",
    cv2.VideoWriter_fourcc(*"mp4v"),
    fps,
    (width, height)
)

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    results = model(frame)
    annotated = results[0].plot()
    out.write(annotated)

cap.release()
out.release()

4. Real-Time Object Detection​

USB Camera​

python3 02.detection_usb_camera.py

CSI Camera​

python3 03.detection_csi_camera.py

5. Best Practices​

  • Use Nano models (yolo11n) for real-time inference
  • Prefer CSI cameras for lower latency
  • Always enable MAX power mode
  • Export models to TensorRT for production

Maintained by HemiHex for Jetson-based advanced vision workflows.