Robust Object Detection
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Benchmarks
Most implemented
YOLOv3: An Incremental Improvement
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
Random Erasing Data Augmentation
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Domain Adaptive Faster R-CNN for Object Detection in the Wild
Papers
AdROD: HyperNetwork-based Adversarially Robust Object Detection for Autonomous Driving
Camera-based object detectors are vulnerable to physical adversarial attacks designed to suppress detections. While adversarial training and input purification offer some protection, they often overfit to specific attack…
Robust Object DetectionAdversarial RobustnessAutonomous DrivingObject TrackingLipSSD: Lipschitz-Constrained Single-Shot Detection for Adversarially Robust Object Detection
Object detectors have many applications in safety-critical systems, but they are known to be sensitive to worst-case perturbations such as adversarial attacks, which limits their applicability in real-world scenarios. Co…
Robust Object DetectionAdversarial RobustnessInfraNet: Quality-Aware RGB Guidance for Efficient Infrared Object Detection
Robust object detection under adverse visual conditions remains a long-standing challenge for multi-modal perception systems. Existing fusion-based methods typically require both RGB and infrared (IR) inputs, and treat t…
Robust Object DetectionFR-DETR: Frequency and Recurrent Feature Refinement for Robust Object Detection under Adverse Weather
Object detection under adverse weather remains challenging due to severe visual degradations and domain shifts. Existing enhancer-based approaches attempt to improve detection by cascading an enhancer with a detector, bu…
Robust Object DetectionFATE: Pillar Encoding and Frequency-Aware Training for Event-Based Object Detection
Event cameras are bio-inspired sensors that asynchronously capture logarithmic intensity changes, offering inherent advantages in high-speed and high-dynamic-range scenarios. However, the sparse and asynchronous nature o…
Robust Object DetectionTiny Collaborative Inference for Occlusion-Robust Object Detection
Edge AI nodes for search and rescue are increasingly expected to run computer vision locally, yet ultra-low-end hardware imposes hard constraints on memory, compute, and inter-device communication. This work addresses oc…
Robust Object DetectionFederated Learning