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:


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 )

Most implemented papers

Domain Adaptive Faster R-CNN for Object Detection in the Wild

yuhuayc/da-faster-rcnn 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

bethgelab/imagecorruptions 17 Jul 2019

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


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 9 Apr 2020

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

reactivetype/simrod ICCV 2021

This paper presents a Simple and effective unsupervised adaptation method for Robust Object Detection (SimROD).

Soft Sampling for Robust Object Detection

starimpact/arm_SNIPER 18 Jun 2018

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 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

a-nau/Plane-Segmentation-Refinement 28 Mar 2020

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 17 Apr 2020

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 31 Aug 2020

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).