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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 4150 of 90 papers

TitleStatusHype
A Robust Learning Approach to Domain Adaptive Object DetectionCode0
Iterative Normalization: Beyond Standardization towards Efficient WhiteningCode0
DPDETR: Decoupled Position Detection Transformer for Infrared-Visible Object DetectionCode0
Soft Sampling for Robust Object DetectionCode0
DyRA: Portable Dynamic Resolution Adjustment Network for Existing DetectorsCode0
Switchable Whitening for Deep Representation LearningCode0
Mind the Backbone: Minimizing Backbone Distortion for Robust Object DetectionCode0
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is ComingCode0
ConstScene: Dataset and Model for Advancing Robust Semantic Segmentation in Construction EnvironmentsCode0
Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement ApproachCode0
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