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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
SAM2Auto: Auto Annotation Using FLASH0
FMG-Det: Foundation Model Guided Robust Object Detection0
VLC Fusion: Vision-Language Conditioned Sensor Fusion for Robust Object Detection0
Learning to Borrow Features for Improved Detection of Small Objects in Single-Shot Detectors0
High Dynamic Range Modulo Imaging for Robust Object Detection in Autonomous Driving0
WS-DETR: Robust Water Surface Object Detection through Vision-Radar Fusion with Detection Transformer0
RobuRCDet: Enhancing Robustness of Radar-Camera Fusion in Bird's Eye View for 3D Object Detection0
UAV Cognitive Semantic Communications Enabled by Knowledge Graph for Robust Object Detection0
Efficient Event-Based Object Detection: A Hybrid Neural Network with Spatial and Temporal Attention0
Evaluating the Adversarial Robustness of Detection Transformers0
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