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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 110 of 90 papers

TitleStatusHype
RoboSense: Large-scale Dataset and Benchmark for Egocentric Robot Perception and Navigation in Crowded and Unstructured EnvironmentsCode2
COCO-O: A Benchmark for Object Detectors under Natural Distribution ShiftsCode2
Random Erasing Data AugmentationCode2
Boosting Domain Generalized and Adaptive Detection with Diffusion Models: Fitness, Generalization, and TransferabilityCode1
LLM-Empowered Embodied Agent for Memory-Augmented Task Planning in Household RoboticsCode1
Robust Object Detection of Underwater Robot based on Domain GeneralizationCode1
Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized DetectionCode1
PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object DetectionCode1
SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface ConditionsCode1
Object-Aware Domain Generalization for Object DetectionCode1
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