# FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection arXiv: https://arxiv.org/abs/2305.17449 · HTML: https://mlsanity.com/paper/2305.17449/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Builds on 13 of its 30 references that are in the map. - First posted on arXiv 27 May 2023. ## Builds on (most cited first, 13 of 13) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014) - [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015) - [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018) - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022) - [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018) - [Drone-based Object Counting by Spatially Regularized Regional Proposal Network](https://mlsanity.com/paper/1707.05972/) (2017) - [End-to-end Learning of Driving Models from Large-scale Video Datasets](https://mlsanity.com/paper/1612.01079/) (2016) - [WoodScape: A multi-task, multi-camera fisheye dataset for autonomous driving](https://mlsanity.com/paper/1905.01489/) (2019) - [You Only Learn One Representation: Unified Network for Multiple Tasks](https://mlsanity.com/paper/2105.04206/) (2021) - [The 5th AI City Challenge](https://mlsanity.com/paper/2104.12233/) (2021) - [Rain Removal in Traffic Surveillance: Does it Matter?](https://mlsanity.com/paper/1810.12574/) (2018) ## Built on by (most cited first, 22 of 22) - [The 8th AI City Challenge](https://mlsanity.com/paper/2404.09432/) (2024) - [The 9th AI City Challenge](https://mlsanity.com/paper/2508.13564/) (2025) - [TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection](https://mlsanity.com/paper/2501.14302/) (2025) - [RICO: Two Realistic Benchmarks and an In-Depth Analysis for Incremental Learning in Object Detection](https://mlsanity.com/paper/2508.13878/) (2025) - [Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets](https://mlsanity.com/paper/2404.10078/) (2024) - [Mcity Data Engine: Iterative Model Improvement Through Open-Vocabulary Data Selection](https://mlsanity.com/paper/2504.21614/) (2025) - [Edge-case Synthesis for Fisheye Object Detection: A Data-centric Perspective](https://mlsanity.com/paper/2507.16254/) (2025) - [Incremental Object Detection with Prompt-based Methods](https://mlsanity.com/paper/2508.14599/) (2025) - [Simple In-place Data Augmentation for Surveillance Object Detection](https://mlsanity.com/paper/2404.11226/) (2024) - [Exploring Surround-View Fisheye Camera 3D Object Detection](https://mlsanity.com/paper/2511.18695/) (2025) - [MULTI: Disentangling Camera Lens, Sensor, View, and Domain for Novel Image Generation](https://mlsanity.com/paper/2605.12134/) (2026) - [From Detection to Understanding: TAR and TAR-Bench for Multi-Task Traffic Anomaly Reasoning](https://mlsanity.com/paper/2608.10317/) (2026) - [UniTraffic-Agent: Unified Traffic Video Reasoning for AI City Challenge 2026 Track 3 with Two Out-of-Domain Evaluations](https://mlsanity.com/paper/2608.13031/) (2026) - [Impact of Video Compression Artifacts on Fisheye Camera Visual Perception Tasks](https://mlsanity.com/paper/2403.16338/) (2024) - [Finding the Reflection Point: Unpadding Images to Remove Data Augmentation Artifacts in Large Open Source Image Datasets for Machine Learning](https://mlsanity.com/paper/2504.03168/) (2025) - [Towards Continual Expansion of Data Coverage: Automatic Text-guided Edge-case Synthesis](https://mlsanity.com/paper/2509.26158/) (2025) - [A Unified Detection Pipeline for Robust Object Detection in Fisheye-Based Traffic Surveillance](https://mlsanity.com/paper/2510.20016/) (2025) - [GTATrack: Winner Solution to SoccerTrack 2025 with Deep-EIoU and Global Tracklet Association](https://mlsanity.com/paper/2602.00484/) (2026) - [Physics-aware Masked Diffusion-based Flood Simulation for Urban Fisheye Disaster Detection](https://mlsanity.com/paper/2607.15527/) (2026) - [Rethinking Pre-Training and Augmentation for Zero-Shot Cross-City Object Detection](https://mlsanity.com/paper/2608.24154/) (2026) - [TAU-Agent: An Agentic Retrieval-Augmented Framework for Traffic Anomaly Understanding](https://mlsanity.com/paper/2608.25935/) (2026) - [FUSEye: Training-Light Fisheye Detection with Overlapping Views and Zero-Initialized Adapters](https://mlsanity.com/paper/2610.02799/) (2026) ## Abstract (arXiv) With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640×640 and 1280×1280, respectively. The dataset will be available on GitHub with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications.