I used SSD for my object detection. But there are some false detection from some other objects in the image. That is happening consistently from same objects. So is there a way to reject those components in training?
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For Yolo, I can do as follow. Just add images with these non-labeled objects to the training dataset and train. Network will learn not to detect such objects.
Also it is desirable to add negative-samples to your training dataset: https://github.com/AlexeyAB/darknet desirable that our training dataset include images with non-labeled objects that we do not want to detect - negative samples without bounded box (empty .txt files). (Credit to alexbe)
In general, what we can do are
Hard negative mining, Inspect confusion matrix and Data Augmentation.
