The following optimization methods can be tried when YOLOE has a missed detection:
- Adjusting the confidence threshold: By
--confParameters lower the threshold (e.g., 0.001) and increase the detection rate - Expanded number of tests: Use
--max_detParameter increases the maximum number of detection targets - Optimize alerts: Using text/visual cues is better than no-cue mode for specific scenarios
- Upgrade model version: Use of larger pre-trained models (e.g., upgrading from version S to version L)
- fine-tuned model: Migration learning on domain data to improve target-specific detection
Suggestions for handling special scenarios:
- For small target detection, it is recommended to input higher resolution images
- For occluded objects, try multi-angle detection or timing analysis
- When the lighting conditions are poor, image enhancement is performed first.
If the problem persists, you can check that the model is loaded correctly and confirm that the input data format meets the requirements.
This answer comes from the articleYOLOE: an open source tool for real-time video detection and segmentation of objectsThe
May not be reproduced without permission:AI productivity tools " What should I do if I encounter a leakage problem when using YOLOE for inspection?
