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 with no code
Robust Object Detection with Multi-input Multi-output Faster R-CNN
In this work, a generalization of the MIMO approach is applied to the task of object detection using the general-purpose Faster R-CNN model.
Towards Robust Object Detection: Bayesian RetinaNet for Homoscedastic Aleatoric Uncertainty Modeling
According to recent studies, commonly used computer vision datasets contain about 4% of label errors.
Scene-aware Learning Network for Radar Object Detection
In this paper, we propose a scene-aware radar learning framework for accurate and robust object detection.
A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection
This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for energy-efficient and robust object detection in resource-constrained platforms.
Multi-Target Domain Adaptation via Unsupervised Domain Classification for Weather Invariant Object Detection
We propose a novel unsupervised domain classification method which can be used to generalize single-target domain adaptation methods to multi-target domains, and design a weather-invariant object detector training framework based on it.
Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty
Reliable uncertainty estimation is crucial for robust object detection in autonomous driving.
Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving
A thermal camera captures an image using the heat difference emitted by objects in the infrared spectrum, and object detection in thermal images becomes effective for autonomous driving in challenging conditions.
Robust Object Detection under Occlusion with Context-Aware CompositionalNets
In this work, we propose to overcome two limitations of CompositionalNets which will enable them to detect partially occluded objects: 1) CompositionalNets, as well as other DCNN architectures, do not explicitly separate the representation of the context from the object itself.
Proposal Learning for Semi-Supervised Object Detection
two-stage object detectors) by training on both labeled and unlabeled data.
Towards Adversarially Robust Object Detection
Object detection is an important vision task and has emerged as an indispensable component in many vision system, rendering its robustness as an increasingly important performance factor for practical applications.
