CODEWITHSUNDEEP
performancetensorflowobject-detectionfaster-rcnn
Vision
Vision

Reputation: 11

How to remove false detection (False Positives) in Faster RCNN

I am using Faster RCNN with Inception V2 on custom dataset. My model is working fine with good detection accuracy. However, I am facing false positive problem when I pass an image to the model I get correct prediction but I am also getting some wrong bounding boxes with high confidence score. Is there any method which can be used as a post-processing to remove these extra detection?

Upvotes: 1

Views: 1016

Answers (2)

Yusuf
Yusuf

Reputation: 41

While testing the model, a value like this in the code

min_score_thresh=0.90

If you set this value to 0.90, it will show results that are 90% or more correct.

Upvotes: 0

Guilherme Correa
Guilherme Correa

Reputation: 594

It seems like this is a common problem with Transfer Learning, you should check out this discussion. In the end it seems that all boils down to what's the source of your false positives.

For instance, once I trained a detector to detect smoke in wildfire images, but it ended up also catching the clouds. To solve that I also annotated the clouds as a new class, and ignored it's detections. This greatly improved the performance.

If it's making wrong detections with high confidence I think it would be hard to solve this problem with only a post-processing.

You could also try hard mining. Altough I'm not really sure how to do it for Faster R-CNN.

Upvotes: 0

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