mlsanityData to 14 Oct 2026

FisheyeDetNet: 360deg Surround view Fisheye Camera based Object Detection System for Autonomous Driving

arXiv 2404.13443 ↗First posted 20 Apr 2024cs.CV cs.ROcited by 2

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Abstract (arXiv)

Object detection is a mature problem in autonomous driving with pedestrian detection being one of the first deployed algorithms. It has been comprehensively studied in the literature. However, object detection is relatively less explored for fisheye cameras used for surround-view near field sensing. The standard bounding box representation fails in fisheye cameras due to heavy radial distortion, particularly in the periphery. To mitigate this, we explore extending the standard object detection output representation of bounding box. We design rotated bounding boxes, ellipse, generic polygon as polar arc/angle representations and define an instance segmentation mIOU metric to analyze these representations. The proposed model FisheyeDetNet with polygon outperforms others and achieves a mAP score of 49.5 % on Valeo fisheye surround-view dataset for automated driving applications. This dataset has 60K images captured from 4 surround-view cameras across Europe, North America and Asia. To the best of our knowledge, this is the first detailed study on object detection on fisheye cameras for autonomous driving scenarios.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 44 of its 56 references that are in the map. First posted on arXiv 20 Apr 2024.

Builds on

44 of 56 references.

References · 44

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32 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Distortion-Aware PETR for BEV Object Detection with Mixed Pinhole-Fisheye Camerascited by 0
  2. 2026FUSEye: Training-Light Fisheye Detection with Overlapping Views and Zero-Initialized Adapterscited by 0