Comparative Analysis of Conventional Object Detection Models on Fisheye Images

Fisheye cameras are increasingly used for object detection and tracking across various sectors. However, fish-eye images present distortions that complicate their processing compared to flat images. Two main approaches exist to tackle this challenge: calibrating fisheye images to convert them into flat images before applying traditional image processing models, or directly employing these models on fisheye images. In this study, we investigated both approaches using two fisheye image databases. Our findings indicate that calibration results in information loss and inadequate time savings. Furthermore, traditional models, despite optimization efforts, exhibit limited performance of approximately 20%, emphasizing the necessity for developing specific algorithms tailored to this technology.