# A Unified Detection Pipeline for Robust Object Detection in Fisheye-Based Traffic Surveillance

arXiv: https://arxiv.org/abs/2510.20016 · HTML: https://mlsanity.com/paper/2510.20016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 27 of 27)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://mlsanity.com/paper/1907.07484/) (2019)
- [YOLOv12: Attention-Centric Real-Time Object Detectors](https://mlsanity.com/paper/2502.12524/) (2025)
- [DETRs with Collaborative Hybrid Assignments Training](https://mlsanity.com/paper/2211.12860/) (2022)
- [Dual Aggregation Transformer for Image Super-Resolution](https://mlsanity.com/paper/2308.03364/) (2023)
- [You Only Learn One Representation: Unified Network for Multiple Tasks](https://mlsanity.com/paper/2105.04206/) (2021)
- [NAFSSR: Stereo Image Super-Resolution Using NAFNet](https://mlsanity.com/paper/2204.08714/) (2022)
- [When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach](https://mlsanity.com/paper/1706.04284/) (2017)
- [Generalized Object Detection on Fisheye Cameras for Autonomous Driving: Dataset, Representations and Baseline](https://mlsanity.com/paper/2012.02124/) (2020)
- [Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement](https://mlsanity.com/paper/2403.16131/) (2024)
- [URIE: Universal Image Enhancement for Visual Recognition in the Wild](https://mlsanity.com/paper/2007.08979/) (2020)
- [FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection](https://mlsanity.com/paper/2305.17449/) (2023)
- [SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection](https://mlsanity.com/paper/1804.00433/) (2018)
- [Attention-based Adaptive Selection of Operations for Image Restoration in the Presence of Unknown Combined Distortions](https://mlsanity.com/paper/1812.00733/) (2018)
- [Traffic Danger Recognition With Surveillance Cameras Without Training Data](https://mlsanity.com/paper/1811.11969/) (2018)
- [Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets](https://mlsanity.com/paper/2404.10078/) (2024)
- [Task-Specific Dual-Model Framework for Comprehensive Traffic Safety Video Description and Analysis](https://mlsanity.com/paper/2510.11907/) (2025)
- [Preprocessing Methods of Lane Detection and Tracking for Autonomous Driving](https://mlsanity.com/paper/2104.04755/) (2021)
- [Prompt-Guided Spatial Understanding with RGB-D Transformers for Fine-Grained Object Relation Reasoning](https://mlsanity.com/paper/2510.11996/) (2025)

## Abstract (arXiv)

Fisheye cameras offer an efficient solution for wide-area traffic surveillance by capturing large fields of view from a single vantage point. However, the strong radial distortion and nonuniform resolution inherent in fisheye imagery introduce substantial challenges for standard object detectors, particularly near image boundaries where object appearance is severely degraded. In this work, we present a detection framework designed to operate robustly under these conditions. Our approach employs a simple yet effective pre and post processing pipeline that enhances detection consistency across the image, especially in regions affected by severe distortion. We train several state-of-the-art detection models on the fisheye traffic imagery and combine their outputs through an ensemble strategy to improve overall detection accuracy. Our method achieves an F1 score of0.6366 on the 2025 AI City Challenge Track 4, placing 8thoverall out of 62 teams. These results demonstrate the effectiveness of our framework in addressing issues inherent to fisheye imagery.