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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

Showing 5160 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-CNN0
The Aircraft Context Dataset: Understanding and Optimizing Data Variability in Aerial DomainsCode1
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling0
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 Detection0
A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection0
Robust Object Detection via Instance-Level Temporal Cycle ConfusionCode1
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective WhiteningCode1
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