ENHANCED OBJECT RECOGNITION IN HEMISPHERICAL IMAGES THROUGH DATA AUGMENTATION AND SYNTHETIC IMAGE INTEGRATION

Abstract

One of the challenges facing the use of fisheye or hemispherical cameras in the task of object recognition despite their wide field of view is the lack of a benchmark dataset and the presence of distortions. Although there are a number of fisheye datasets avail able, they are usually focused on specific object recognition tasks. This is evident in the case of KITTI-360 focused on autonomous driving and THEODORE which is fo cused on person recognition. In this work, Fisheye365, a dataset for generalized object recognition is proposed. It contains 5.1 million images obtained by applying 8 differ ent transformations to the original Objects365 dataset. For testing purposes, COHI, a benchmark fisheye testing dataset is modified to COHI-365. Data-centric methods are applied to improve the performance of the YOLOv7 model on perspective and fish eye images. The YOLOv7 model trained on Fisheye365, YOLOv7-T2, outperforms the YOLOv7 model trained only on Objects365, YOLOv7-0 by 4.8% when tested on COHI-365. Finally, to further improve the performance of YOLOv7-T2, 16,000 synthetic images containing 13 iconic classes lying at the intersection of underrepre sented and underperforming classes of Objects365 were generated. YOLOv7-T2 was finetuned on this synthetic dataset and had a least performance of 21% on 8 out of the 13 classes