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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 76–90 of 90 papers

TitleStatusHype
Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving—0
Robust Object Detection under Occlusion with Context-Aware CompositionalNets—0
Proposal Learning for Semi-Supervised Object Detection—0
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
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
Soft Sampling for Robust Object DetectionCode0
Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection—0
Dropout Sampling for Robust Object Detection in Open-Set Conditions—0
Modelling Observation Correlations for Active Exploration and Robust Object Detection—0
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