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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 61–70 of 90 papers

TitleStatusHype
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
VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection—0
Improving Batch Normalization with TTA for Robust Object Detection in Self-Driving—0
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