search

skokec / segdec-net-jim2019

Surface Defect Detection with Segmentation-Decision Network on KolektorSDD
Other
135 stars 45 forks source link

Have question about the decision network #21

Open bill2239 opened 4 years ago

bill2239 commented 4 years ago

Hi, Thanks for the great work, but I have question about the decision network and how it help with segmentation. According to your paper, segmentation network is trained separately. When decision network is trained, weights for segmentation network are frozen. So I am wondering if decision network help segmentation network at all or it just do classification. Thanks

onlyNata commented 4 years ago

You can understand it as attention mechanism. Semantic segmentation can focus on small defects, but direct classification can't.

bill2239 commented 4 years ago

I thought the ultimate goal is producing a good segmentation mask, so I am wondering what the purpose of the decision network in this scenario. Maybe I missed something in there.

onlyNata commented 4 years ago

Because the result of semantic segmentation is not good enough, it can not distinguish background and defect accurately(the defect too small and low number of samples). then decision network refine the results(using multi-label classified algorith ).

bill2239 commented 4 years ago

That's where the question is, the decision network is trained with segmentation network's weight frozen. So how training the decision network would improve segmentation result in this way.

onlyNata commented 4 years ago

OK, I see.

"I thought the ultimate goal is producing a good segmentation mask, ..."

No,this is a classification prediction issue.Semantic segmentation is an auxiliary means not ultimate goal. In other words, semantic segmentation is not necessary if the defects are large enough. the decision network would not improve segmentation result ( a better mask),but reuse the seg-feature(train with segmentation network's weight frozen) can make the classification prediction more accurate.

skokec commented 4 years ago

Hi,

thanks @onlyNata for explained it quite well.

The purpose is NOT segmentation but image level classification. Segmentation is only used as auxiliary step to get good features for classification, since classification will have issues due to small sample sizes limitations that often happens in industrial applications.

In our on-going work, we are doing end-to-end learning, i.e., both segmentation and classification at the same time, but even then it proved better that decision net has minimal or no influence on learning of the segmentation layers.

Best, Domen