Feature-aligned Fisheye Object Detection Network for Autonomous Driving

Abstract

Fisheye cameras, renowned for their panoramic field of view (FOV) of 360°, are crucial for surround-view perception in autonomous driving. However, research on object perception in fisheye images lags behind that of standard images. To address this gap, we propose a feature-aligned fisheye object detection network specifically tailored for autonomous driving. Current fisheye perception algorithms often overlook the misalignment issues that typically arise in object detectors. To tackle these challenges in the feature pyramid network (FPN), we introduce a feature-aligned pyramid module (FaPM), which learns pixel transformation offsets to contextually align feature maps. Additionally, we present a location-aligned detection head (LaDH) to align the spatial distribution of classification and regression localization. Integrating these modules into a detection framework results in a novel feature-aligned fisheye object detector. Our method undergoes extensive evaluation on the WoodScape dataset, achieving a mean average precision (mAP) of 32.2%, surpassing the performance of existing methods.