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
Most implemented papers
YOLOv3: An Incremental Improvement
At 320x320 YOLOv3 runs in 22 ms at 28. 2 mAP, as accurate as SSD but three times faster.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
rbgirshick/py-faster-rcnn
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NeurIPS 2015
In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals.
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
XingangPan/IBN-Net
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ECCV 2018
IBN-Net carefully integrates Instance Normalization (IN) and Batch Normalization (BN) as building blocks, and can be wrapped into many advanced deep networks to improve their performances.
Random Erasing Data Augmentation
zhunzhong07/Random-Erasing
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In this paper, we introduce Random Erasing, a new data augmentation method for training the convolutional neural network (CNN).
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
google-research/augmix
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ICLR 2020
We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions.
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.
Learning Data Augmentation Strategies for Object Detection
Importantly, the best policy found on COCO may be transferred unchanged to other detection datasets and models to improve predictive accuracy.
Iterative Normalization: Beyond Standardization towards Efficient Whitening
huangleiBuaa/IterNorm
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CVPR 2019
With the support of SND, we provide natural explanations to several phenomena from the perspective of optimization, e. g., why group-wise whitening of DBN generally outperforms full-whitening and why the accuracy of BN degenerates with reduced batch sizes.
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.
