Abstract
Various camera types, from linear perspective to wide field of view (FoV), panoramic, and hemispherical, are utilized in computer vision due to their distinct features. Images obtained from a perspective camera, which mimics the human eye are dominant in benchmark datasets such as ImageNet and MS COCO. Others obtained from wide FoV cameras are present in surveillance and person detection datasets, and autonomous driving. The proliferation of visual data acquisition through cameras with diverse FoVs has posed a significant challenge to the development of robust object recognition models. This heterogeneity necessitates the creation of an object recognition engine that can seamlessly adapt to the complexities introduced by different viewpoints, distortions, and angle variations. In this work, we leverage data augmentation to enhance the robustness of the YOLOv7 model when confronted with various camera types, such as perspective variations, distortions, and diverse viewing angles in images. We created the RMFV365 dataset, comprising 5.1 million images that encompass a broad spectrum of perspectives and distortions. This dataset serves as a valuable resource for training the model in generalized object recognition tasks. For testing purposes, we modified COHI, a benchmark fisheye testing dataset to COHI-365. The YOLOv7 model trained on RMFV365 (YOLOv7-T2) outperforms the YOLOv7 model trained on Objects365 (YOLOv7-Base), achieving a 4.8% improvement as measured by the mAP50 metric when tested on COHI-365. Also, YOLOv7-T2 achieves a close result as YOLOv7-Base when tested on the original Objects365 data.