SOTAVerified

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 2130 of 90 papers

TitleStatusHype
DPDETR: Decoupled Position Detection Transformer for Infrared-Visible Object DetectionCode0
SDNIA-YOLO: A Robust Object Detection Model for Extreme Weather Conditions0
SemanticSpray++: A Multimodal Dataset for Autonomous Driving in Wet Surface ConditionsCode1
ConstScene: Dataset and Model for Advancing Robust Semantic Segmentation in Construction EnvironmentsCode0
Object-Aware Domain Generalization for Object DetectionCode1
SimMining-3D: Altitude-Aware 3D Object Detection in Complex Mining Environments: A Novel Dataset and ROS-Based Automatic Annotation Pipeline0
Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer LearningCode1
DyRA: Portable Dynamic Resolution Adjustment Network for Existing DetectorsCode0
On the Robustness of Object Detection Models on Aerial ImagesCode1
Improved Region Proposal Network for Enhanced Few-Shot Object DetectionCode1
Show:102550
← PrevPage 3 of 9Next →

No leaderboard results yet.