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
SimMining-3D: Altitude-Aware 3D Object Detection in Complex Mining Environments: A Novel Dataset and ROS-Based Automatic Annotation Pipeline
To overcome these challenges, 3D object detection using point cloud data has emerged as a comprehensive approach.
FROD: Robust Object Detection for Free
Object detection is a vital task in computer vision and has become an integral component of numerous critical systems.
Uncertainty-Encoded Multi-Modal Fusion for Robust Object Detection in Autonomous Driving
Multi-modal fusion has shown initial promising results for object detection of autonomous driving perception.
Multi-Task Cross-Modality Attention-Fusion for 2D Object Detection
In addition, we introduce a Multi-Task Cross-Modality Attention-Fusion Network (MCAF-Net) for object detection, which includes two new fusion blocks.
SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection
In this paper, we introduce Semantic Reasoning with Compound Domains (SRCD) for Single-DGOD.
SF-FSDA: Source-Free Few-Shot Domain Adaptive Object Detection with Efficient Labeled Data Factory
Domain adaptive object detection aims to leverage the knowledge learned from a labeled source domain to improve the performance on an unlabeled target domain.
On the Importance of Backbone to the Adversarial Robustness of Object Detectors
We argue that using adversarially pre-trained backbone networks is essential for enhancing the adversarial robustness of object detectors.
A Semantic Consistency Feature Alignment Object Detection Model Based on Mixed-Class Distribution Metrics
Then, a Semantic Consistency Feature Alignment Model (SCFAM) based on mixed-classes $H-divergence$ was also presented.
Weakly Aligned Feature Fusion for Multimodal Object Detection
In this article, we propose a general multimodal detector named aligned region CNN (AR-CNN) to tackle the position shift problem.
RestoreX-AI: A Contrastive Approach towards Guiding Image Restoration via Explainable AI Systems
Modern applications such as self-driving cars and drones rely heavily upon robust object detection techniques.
