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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 71–80 of 90 papers

TitleStatusHype
SAM2Auto: Auto Annotation Using FLASH—0
Scene-aware Learning Network for Radar Object Detection—0
FMG-Det: Foundation Model Guided Robust Object Detection—0
FROD: Robust Object Detection for Free—0
A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection—0
Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection—0
SDNIA-YOLO: A Robust Object Detection Model for Extreme Weather Conditions—0
High Dynamic Range Modulo Imaging for Robust Object Detection in Autonomous Driving—0
Segmentation is All You Need—0
On the Importance of Backbone to the Adversarial Robustness of Object DetectorsCode0
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