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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 61–70 of 90 papers

TitleStatusHype
Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving—0
Weakly Aligned Feature Fusion for Multimodal Object Detection—0
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
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