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 )
Libraries
Use these libraries to find Robust Object Detection models and implementationsDatasets
Latest papers
ConstScene: Dataset and Model for Advancing Robust Semantic Segmentation in Construction Environments
The increasing demand for autonomous machines in construction environments necessitates the development of robust object detection algorithms that can perform effectively across various weather and environmental conditions.
Object-Aware Domain Generalization for Object Detection
To address these problems, we propose an object-aware domain generalization (OA-DG) method for single-domain generalization in object detection.
DyRA: Portable Dynamic Resolution Adjustment Network for Existing Detectors
This paper introduces DyRA, a dynamic resolution adjustment network providing an image-specific scale factor for existing detectors.
On the Robustness of Object Detection Models in Aerial Images
The robustness of object detection models is a major concern when applied to real-world scenarios.
Improved Region Proposal Network for Enhanced Few-Shot Object Detection
Specifically, we develop a hierarchical ternary classification region proposal network (HTRPN) to localize the potential unlabeled novel objects and assign them new objectness labels to distinguish these objects from the base training dataset classes.
COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts
To give a more comprehensive robustness assessment, we introduce COCO-O(ut-of-distribution), a test dataset based on COCO with 6 types of natural distribution shifts.
Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection
We propose to use Relative Gradient Norm (RGN) as a way to measure the vulnerability of a backbone to feature distortion, and show that high RGN is indeed correlated with lower OOD performance.
Identification of Novel Classes for Improving Few-Shot Object Detection
Our improved hierarchical sampling strategy for the region proposal network (RPN) also boosts the perception ability of the object detection model for large objects.
Towards Scene Understanding for Autonomous Operations on Airport Aprons
The results are quite promising for future applications and provide essential insights regarding the selection of aggregation strategies as well as current potentials and limitations of similar approaches in this research domain.
Robust Object Detection in Remote Sensing Imagery with Noisy and Sparse Geo-Annotations (Full Version)
In order to create the necessary training annotations for object detectors, imagery can be georeferenced and combined with data from other sources, such as points of interest localized by GPS sensors.