Is there any problem if I run this code on a jetson xavier? about yolov8_ros HOT 10 OPEN

Hi @goodhsm2000, it seems that YOLOv8 is detecting nothing in your images.
You can try reducing the threshold. Your objects may be getting bad scores.
Have you checked if the images obtained from the real sense camera are correct? I mean visualizing them.

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Yes. I used the cv2.imshow command to check that the camera image is coming in well. I set the Threshold to 0.5, but it can't even detect the person right in front of the camera.

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Try a threshold below 0.5. Which ultralytics version are you using?

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The same problem occurs when the conf value is 0.1. Ultralytics is 8.0.57. version

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You can try a new version of ultralytics. I have published a new branch (devel) with ultralytics 8.0.149.
Btw, I also recommend using Humble vision_msgs package. You can clone the repo in your ros2_ws.

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Hello. There was a problem that I couldn't detect any objects just like before even using the code from the new branch.
While analyzing the code for troubleshooting, I found that the problem occurred in the "self.yolo.to (device)" part.
Strangely, in my jetson xavier, the YOLO model was not transferred to "cuda:0" with this code.
I erased the "self.yolo.to(device)" part and solved the problem using device="cuda:0" as a argument for the YOLO's "predict" function.

Do you know why this problem happened?

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I have tried using the device in the predict function and it works well, I may change it in the future.
Btw, have you checked ultralytics/ultralytics#3557?

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Yes. What is certain is that when I executed the code currently in the main branch in my laptop environment, it worked without any problems.

I haven't used Deepstream yet, so I haven't read the ultralytics/ultralytics#3557 in detail.

Are you suggesting that I use Deepstream?

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No, I was thinking about the jetpack version since they suggest to update it. Nevertheless, I have changed the use of the device from the model creation to the prediction function as you mentioned before in the devel branch. If you want to try it, I have also created some custom messages as I have received feedback from different sources about the differences in the vision_msgs package from Foxy to Humble. You can also try instance segmentation and hume pose that are available in the devel branch.

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I see. But I was already using the jetpack 5.1 version.

I checked the code of the new devel branch you uploaded. Thank you very, very much for your quick response and attentive assistance. It was a great help in resolving the issue.

And thank you for sharing the good code.

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