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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 7690 of 90 papers

TitleStatusHype
Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection0
SDNIA-YOLO: A Robust Object Detection Model for Extreme Weather Conditions0
High Dynamic Range Modulo Imaging for Robust Object Detection in Autonomous Driving0
Segmentation is All You Need0
On the Importance of Backbone to the Adversarial Robustness of Object DetectorsCode0
Soft Sampling for Robust Object DetectionCode0
DyRA: Portable Dynamic Resolution Adjustment Network for Existing DetectorsCode0
Mind the Backbone: Minimizing Backbone Distortion for Robust Object DetectionCode0
DPDETR: Decoupled Position Detection Transformer for Infrared-Visible Object DetectionCode0
Iterative Normalization: Beyond Standardization towards Efficient WhiteningCode0
Switchable Whitening for Deep Representation LearningCode0
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is ComingCode0
A Robust Learning Approach to Domain Adaptive Object DetectionCode0
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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