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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 3140 of 90 papers

TitleStatusHype
Radar+RGB Attentive Fusion for Robust Object Detection in Autonomous VehiclesCode1
Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial VehiclesCode1
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection SystemsCode1
Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge DetectionCode1
AugMix: A Simple Data Processing Method to Improve Robustness and UncertaintyCode1
Learning Data Augmentation Strategies for Object DetectionCode1
Two at Once: Enhancing Learning and Generalization Capacities via IBN-NetCode1
YOLOv3: An Incremental ImprovementCode1
Domain Adaptive Faster R-CNN for Object Detection in the WildCode1
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksCode1
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