Defending Object Detection Models Against Image Distortions

Mark Ofori-Oduro, Maria Amer; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 3854-3863

Image distortions pose a significant challenge to object detection. To address this issue, our paper introduces a novel data augmentation method that generates new samples resembling the original training images. The new sample exhibits randomly altered pixels based on a pixel distribution obtained from multiple image distortions using kernel density estimation (KDE). The main steps of our method, GSES, are generating distorted versions of each pixel of an original training image, selecting a set of pixels in each version, and then, for each selected pixel, estimating its distribution using KDE and then sampling one pixel from this distribution. By employing this approach, the new samples possess distorted pixels while maintaining a certain degree of similarity to the original image. This degree of similarity is essential to balance the accuracy of object detection models under distorted and clean images. Our approach improves the accuracy of different object detection models under 15 image distortions, such as motion blur, fog, and noise. For example, the average accuracy of YOLOv4 improves by 9.19% and 9.54% across all 15 distortions added to the COCO and PASCAL datasets, respectively. Our method surpasses other defence methods to combat image distortions. Our ablation and stability studies show why our method performs well. Moreover, we also show that our method can be well used to improve the accuracy of image classification under 15 distortions and cross-domains. Our code is available at https://github.com/moforio/GSES/.