Saraswathi Sivamani1, Sun Il Chon1, Do Yeon Choi1, Dong Hoon Lee2, Ji Hwan Park1,*
CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4433-4452, 2022, DOI:10.32604/cmc.2022.026759
Abstract Existing segmentation and augmentation techniques on convolutional neural network (CNN) has produced remarkable progress in object detection. However, the nominal accuracy and performance might be downturned with the photometric variation of images that are directly ignored in the training process, along with the context of the individual CNN algorithm. In this paper, we investigate the effect of a photometric variation like brightness and sharpness on different CNN. We observe that random augmentation of images weakens the performance unless the augmentation combines the weak limits of photometric variation. Our approach has been justified by the experimental result obtained from the PASCAL… More >
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