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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
Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations (Full Version)Code1
Robust Object Detection With Inaccurate Bounding BoxesCode1
A Semantic Consistency Feature Alignment Object Detection Model Based on Mixed-Class Distribution Metrics0
Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object DetectionCode1
Weakly Aligned Feature Fusion for Multimodal Object Detection0
Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic SegmentationCode1
RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems0
Fusing Event-based and RGB camera for Robust Object Detection in Adverse ConditionsCode1
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic MaskingCode1
Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-DistillationCode1
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