DATA AUGMENTATION USING ARTIFICIAL IMMUNE SYSTEMS FOR NOISE-ROBUST CNN MODELS

CNN based models are the state of the art in object detection. The effect of noise on their performances has not been extensively examined. In this paper, we examine the models SSD, Yolo, and Faster RCNN, and show that their performance dropped greatly under white noise by an average mAP of 10.33 on the PASCAL-VOC dataset. We propose mitigating this issue by augmenting the training dataset with antibodies generated using Artificial Immune Systems (AIS). We then test the CNN models under different noise levels and show that our data augmentation approach significantly improves their performance under noise by more than 55%, i.e., the average mAP drop reduced to 4.37, without altering their speed. We also show that training of said CNN models under noise does improve their performance but interestingly less than when using our data augmentation AIS approach.