Robust Object Detection

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

Libraries

Datasets

Subtasks

Most implemented papers

YOLOv3: An Incremental Improvement

Content

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Random Erasing Data Augmentation

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Learning Data Augmentation Strategies for Object Detection

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

Iterative Normalization: Beyond Standardization Towards Efficient Whitening

Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net