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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 71–80 of 90 papers

TitleStatusHype
Incremental Deep Learning for Robust Object Detection in Unknown Cluttered Environments—0
Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty—0
SF-FSDA: Source-Free Few-Shot Domain Adaptive Object Detection with Efficient Labeled Data Factory—0
Learning to Borrow Features for Improved Detection of Small Objects in Single-Shot Detectors—0
SimMining-3D: Altitude-Aware 3D Object Detection in Complex Mining Environments: A Novel Dataset and ROS-Based Automatic Annotation Pipeline—0
Modelling Observation Correlations for Active Exploration and Robust Object Detection—0
Multimodal Object Detection using Depth and Image Data for Manufacturing Parts—0
Multi-Target Domain Adaptation via Unsupervised Domain Classification for Weather Invariant Object Detection—0
WS-DETR: Robust Water Surface Object Detection through Vision-Radar Fusion with Detection Transformer—0
Multi-Task Cross-Modality Attention-Fusion for 2D Object Detection—0
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