Robust Object Detection
42 papers with code • 5 benchmarks • 9 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Robust Object Detection
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Libraries
Use these libraries to find Robust Object Detection models and implementationsDatasets
MS COCO
Cityscapes
PASCAL VOC 2007
DWD
RF100
Separated COCO
Aircraft Context Dataset
Occluded COCO
Apron Dataset
Latest papers
Robust Object Detection With Inaccurate Bounding Boxes
cxliu0/oa-mil
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As the crowd-sourcing labeling process and the ambiguities of the objects may raise noisy bounding box annotations, the object detectors will suffer from the degenerated training data.
Robust Environment Perception for Automated Driving: A Unified Learning Pipeline for Visual-Infrared Object Detection
samvadidar/rgbt
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The RGB complementary metal-oxidesemiconductor (CMOS) sensor works within the visible light spectrum.
Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation
helioszhao/shade
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Furthermore, we present a novel style hallucination module (SHM) to generate style-diversified samples that are essential to consistency learning.
Fusing Event-based and RGB camera for Robust Object Detection in Adverse Conditions
abhishek1411/event-rgb-fusion
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ICRA 2022
The ability to detect objects, under image corruptions and different weather conditions is vital for deep learning models especially when applied to real-world applications such as autonomous driving.
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking
inspire-group/ObjectSeeker
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An attacker can use a single physically-realizable adversarial patch to make the object detector miss the detection of victim objects and undermine the functionality of object detection applications.
Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation
amingwu/single-dgod
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CVPR 2022
Particularly, for the night-sunny scene, our method outperforms baselines by 3%, which indicates that our method is instrumental in enhancing generalization ability.
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images
qxgeng/dea-net
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On DOTA, our DEA-Net which integrated with the baseline of RoI-Transformer surpasses the advanced method by 0. 40% mean-Average-Precision (mAP) for oriented object detection with a weaker backbone network (ResNet-101 vs ResNet-152) and 3. 08% mean-Average-Precision (mAP) for horizontal object detection with the same backbone.
The Aircraft Context Dataset: Understanding and Optimizing Data Variability in Aerial Domains
Despite their increasing demand for assistant and autonomous systems, the recent shift towards data-driven approaches has hardly reached aerial domains, partly due to a lack of specific training and test data.
SimROD: A Simple Adaptation Method for Robust Object Detection
This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD).
Exploring Sequence Feature Alignment for Domain Adaptive Detection Transformers
In DQFA, a novel domain query is used to aggregate and align global context from the token sequence of both domains.
