Robust Object Detection
27 papers with code • 3 benchmarks • 8 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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Datasets
COCO
Cityscapes
PASCAL VOC
PASCAL VOC 2007
RF100
Occluded COCO
Separated COCO
Aircraft Context Dataset
Most implemented papers
Domain Adaptive Faster R-CNN for Object Detection in the Wild
yuhuayc/da-faster-rcnn
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CVPR 2018
The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.
Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
The ability to detect objects regardless of image distortions or weather conditions is crucial for real-world applications of deep learning like autonomous driving.
Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach
TAMU-VITA/UAV-NDFT
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ICCV 2019
Object detection from images captured by Unmanned Aerial Vehicles (UAVs) is becoming increasingly useful.
TOG: Targeted Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems
git-disl/TOG
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The rapid growth of real-time huge data capturing has pushed the deep learning and data analytic computing to the edge systems.
SimROD: A Simple Adaptation Method for Robust Object Detection
This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD).
Soft Sampling for Robust Object Detection
Interestingly, we observe that after dropping 30% of the annotations (and labeling them as background), the performance of CNN-based object detectors like Faster-RCNN only drops by 5% on the PASCAL VOC dataset.
A Robust Learning Approach to Domain Adaptive Object Detection
Gabriel-Macias/robust_frcnn
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ICCV 2019
To adapt to the domain shift, the model is trained on the target domain using a set of noisy object bounding boxes that are obtained by a detection model trained only in the source domain.
Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection
Our approach is motivated by logistics, where this assumption is valid and refined planes can be used to perform robust object detection without the need for supervised learning.
Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles
fkthi/OpenTrafficMonitoringPlus
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A robust object detection is crucial for reliable results, hence the state-of-the-art deep neural network Mask-RCNN is applied for that purpose.
Radar+RGB Attentive Fusion for Robust Object Detection in Autonomous Vehicles
RituYadav92/Radar-RGB-Attentive-Multimodal-Object-Detection
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BIRANet yields 72. 3/75. 3% average AP/AR on the NuScenes dataset, which is better than the performance of our base network Faster-RCNN with Feature pyramid network(FFPN).
