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Importance of Adaptive Photometric Augmentation for Different Convolutional Neural Network

Saraswathi Sivamani1, Sun Il Chon1, Do Yeon Choi1, Dong Hoon Lee2, Ji Hwan Park1,*

1 AI Reliability Lab, ThinkforBL Consultancy Services, Gangnam, Seoul, Korea
2 Department of Biosystems Engineering, Chungbuk National University, Cheongju-si, Chungcheongbuk-do, 28644, Korea

* Corresponding Author: Ji Hwan Park. Email:

Computers, Materials & Continua 2022, 72(3), 4433-4452.


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 VOC 2007 dataset, with object detection CNN algorithms such as YOLOv3 (You Only Look Once), Faster R-CNN (Region-based CNN), and SSD (Single Shot Multibox Detector). Each CNN model shows performance loss for varying sharpness and brightness, ranging between −80% to 80%. It was further shown that compared to random augmentation, the augmented dataset with weak photometric changes delivered high performance, but the photometric augmentation range differs for each model. Concurrently, we discuss some research questions that benefit the direction of the study. The results prove the importance of adaptive augmentation for individual CNN model, subjecting towards the robustness of object detection.


Cite This Article

S. Sivamani, S. Il Chon, D. Yeon Choi, D. Hoon Lee and J. Hwan Park, "Importance of adaptive photometric augmentation for different convolutional neural network," Computers, Materials & Continua, vol. 72, no.3, pp. 4433–4452, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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