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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 51–60 of 90 papers

TitleStatusHype
On the Importance of Backbone to the Adversarial Robustness of Object DetectorsCode0
Towards Adversarially Robust Object Detection—0
Robust Object Detection under Occlusion with Context-Aware CompositionalNets—0
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling—0
Towards Robust Object Detection: Identifying and Removing Backdoors via Module Inconsistency Analysis—0
Dropout Sampling for Robust Object Detection in Open-Set Conditions—0
Efficient Event-Based Object Detection: A Hybrid Neural Network with Spatial and Temporal Attention—0
Evaluating the Adversarial Robustness of Detection Transformers—0
Robust Object Detection with Multi-input Multi-output Faster R-CNN—0
SAM2Auto: Auto Annotation Using FLASH—0
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