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Robust Object Detection

A Benchmark for the: Robustness of Object Detection Models to Image Corruptions and Distortions

To allow fair comparison of robustness enhancing methods all models have to use a standard ResNet50 backbone because performance strongly scales with backbone capacity. If requested an unrestricted category can be added later.

Benchmark Homepage: https://github.com/bethgelab/robust-detection-benchmark

Metrics:

mPC [AP]: Mean Performance under Corruption [measured in AP]

rPC [%]: Relative Performance under Corruption [measured in %]

Test sets: Coco: val 2017; Pascal VOC: test 2007; Cityscapes: val;

( Image credit: Benchmarking Robustness in Object Detection )

Papers

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Showing 51–90 of 90 papers

TitleStatusHype
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial ImagesCode1
Robust Object Detection with Multi-input Multi-output Faster R-CNN—0
The Aircraft Context Dataset: Understanding and Optimizing Data Variability in Aerial DomainsCode1
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling—0
SimROD: A Simple Adaptation Method for Robust Object DetectionCode1
Exploring Sequence Feature Alignment for Domain Adaptive Detection TransformersCode1
Scene-aware Learning Network for Radar Object Detection—0
A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection—0
Robust Object Detection via Instance-Level Temporal Cycle ConfusionCode1
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective WhiteningCode1
Multi-Target Domain Adaptation via Unsupervised Domain Classification for Weather Invariant Object Detection—0
DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding AttacksCode1
FloW: A Dataset and Benchmark for Floating Waste Detection in Inland WatersCode1
Radar+RGB Attentive Fusion for Robust Object Detection in Autonomous VehiclesCode1
Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty—0
Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving—0
Robust Object Detection under Occlusion with Context-Aware CompositionalNets—0
Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial VehiclesCode1
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection SystemsCode1
Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge DetectionCode1
Proposal Learning for Semi-Supervised Object Detection—0
AugMix: A Simple Data Processing Method to Improve Robustness and UncertaintyCode1
Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement ApproachCode0
Towards Adversarially Robust Object Detection—0
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is ComingCode0
Learning Data Augmentation Strategies for Object DetectionCode1
Segmentation is All You Need—0
Switchable Whitening for Deep Representation LearningCode0
Iterative Normalization: Beyond Standardization towards Efficient WhiteningCode0
A Robust Learning Approach to Domain Adaptive Object DetectionCode0
Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments—0
Two at Once: Enhancing Learning and Generalization Capacities via IBN-NetCode1
Soft Sampling for Robust Object DetectionCode0
YOLOv3: An Incremental ImprovementCode1
Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection—0
Domain Adaptive Faster R-CNN for Object Detection in the WildCode1
Dropout Sampling for Robust Object Detection in Open-Set Conditions—0
Random Erasing Data AugmentationCode2
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksCode1
Modelling Observation Correlations for Active Exploration and Robust Object Detection—0
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