Road Object Detection Robust to Distorted Objects at the Edge Regions of Images

Wooksu Shin, Donghyuk Choi, Hancheol Park, Jeongho Kim

Abstract: Fish-eye cameras, capable of capturing wide areas, enable efficient traffic monitoring with only a few cameras. Nevertheless, it still remains challenging to successfully detect objects in images from such cameras. In this work, we analyze key reasons why object detectors frequently make incorrect predictions in such images and propose methods to address them. More specifically, we address the issues of objects being represented as smaller at the edges of images and the distortion of non-target objects (e.g., street signs), which are recognized as target objects (e.g., vehicles). Furthermore, in this work, we propose a road object detector capable of achieving high performance by additionally applying various techniques known to generally enhance detection performance. Our proposed detector achieved second place in Track 4 of the 2024 AI City Challenge with an F1 score of 0.6196. Our code is publicly available athttps://github.com/nota-github/AIC2024_Track4_Nota.