IoUCert: Robustness Verification for Anchor-based Object Detectors

• URL: http://arxiv.org/abs/2603.03043v2
• Date: Wed, 04 Mar 2026 22:37:20 GMT
• Title: IoUCert: Robustness Verification for Anchor-based Object Detectors
• Authors: Benedikt Brückner, Alejandro J. Mercado, Yanghao Zhang, Panagiotis Kouvaros, Alessio Lomuscio,
• Abstract summary: We introduce IoUCert, a novel formal verification framework designed specifically to overcome these bottlenecks in anchor-based object detection architectures.<n>We show that our method enables the robustness verification of realistic, anchor-based models including SSD, YOLOv2, and YOLOv3 variants against various input perturbations.
• Score: 58.35703549470485
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: While formal robustness verification has seen significant success in image classification, scaling these guarantees to object detection remains notoriously difficult due to complex non-linear coordinate transformations and Intersection-over-Union (IoU) metrics. We introduce IoUCert, a novel formal verification framework designed specifically to overcome these bottlenecks in foundational anchor-based object detection architectures. Focusing on the object localisation component in single-object settings, we propose a coordinate transformation that enables our algorithm to circumvent precision-degrading relaxations of non-linear box prediction functions. This allows us to optimise bounds directly with respect to the anchor box offsets which enables a novel Interval Bound Propagation method that derives optimal IoU bounds. We demonstrate that our method enables, for the first time, the robustness verification of realistic, anchor-based models including SSD, YOLOv2, and YOLOv3 variants against various input perturbations.

Related papers

• Source-Free Object Detection with Detection Transformer [59.33653163035064]
  Source-Free Object Detection (SFOD) enables knowledge transfer from a source domain to an unsupervised target domain for object detection without access to source data.<n>Most existing SFOD approaches are either confined to conventional object detection (OD) models like Faster R-CNN or designed as general solutions without tailored adaptations for novel OD architectures, especially Detection Transformer (DETR)<n>In this paper, we introduce Feature Reweighting ANd Contrastive Learning NetworK (FRANCK), a novel SFOD framework specifically designed to perform query-centric feature enhancement for DETRs.
  arXiv  Detail & Related papers  (2025-10-13T07:35:04Z)
• Efficient Feature Fusion for UAV Object Detection [9.632727117779178]
  Small objects, in particular, occupy small portions of images, making their accurate detection difficult.<n>Existing multi-scale feature fusion methods address these challenges by aggregating features across different resolutions.<n>We propose a novel feature fusion framework specifically designed for UAV object detection tasks.
  arXiv  Detail & Related papers  (2025-01-29T20:39:16Z)
• Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment [59.61554561979589]
  Edge computing has emerged as a key paradigm for deploying deep learning-based object detection in time-sensitive scenarios.<n>Existing edge detection methods face challenges: difficulty balancing detection precision with lightweight models, limited adaptability, and insufficient real-world validation.<n>We propose the Edge Detection Toolbox (ED-TOOLBOX), which utilizes generalizable plug-and-play components to adapt object detection models for edge environments.
  arXiv  Detail & Related papers  (2024-12-24T07:28:10Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• Decoupled IoU Regression for Object Detection [31.9114940121939]
  Non-maximum suppression (NMS) is widely used in object detection pipelines for removing duplicated bounding boxes. Inconsistency between the confidence for NMS and the real localization confidence seriously affects detection performance. We propose a novel Decoupled IoU Regression model to handle these problems.
  arXiv  Detail & Related papers  (2022-02-02T04:01:11Z)
• Aligning Pretraining for Detection via Object-Level Contrastive Learning [57.845286545603415]
  Image-level contrastive representation learning has proven to be highly effective as a generic model for transfer learning. We argue that this could be sub-optimal and thus advocate a design principle which encourages alignment between the self-supervised pretext task and the downstream task. Our method, called Selective Object COntrastive learning (SoCo), achieves state-of-the-art results for transfer performance on COCO detection.
  arXiv  Detail & Related papers  (2021-06-04T17:59:52Z)
• Pseudo-IoU: Improving Label Assignment in Anchor-Free Object Detection [60.522877583407904]
  Current anchor-free object detectors are quite simple and effective yet lack accurate label assignment methods. We present Pseudo-Intersection-over-Union(Pseudo-IoU): a simple metric that brings more standardized and accurate assignment rule into anchor-free object detection frameworks. Our method achieves comparable performance to other recent state-of-the-art anchor-free methods without bells and whistles.
  arXiv  Detail & Related papers  (2021-04-29T02:48:47Z)
• CRAUM-Net: Contextual Recursive Attention with Uncertainty Modeling for Salient Object Detection [0.0]
  We present a novel framework that integrates multi-scale context aggregation, advanced attention mechanisms, and an uncertainty-aware module for improved SOD performance.<n>Our Adaptive Cross-Scale Context Module effectively fuses features from multiple levels, leveraging Recursive Channel Spatial Attention and Convolutional Block Attention.<n>To train our network robustly, we employ a combination of boundary-sensitive and topology-preserving loss functions, including Boundary IoU, Focal Tversky, and Topological Saliency losses.
  arXiv  Detail & Related papers  (2020-06-04T18:33:59Z)
• Scope Head for Accurate Localization in Object Detection [135.9979405835606]
  We propose a novel detector coined as ScopeNet, which models anchors of each location as a mutually dependent relationship. With our concise and effective design, the proposed ScopeNet achieves state-of-the-art results on COCO.
  arXiv  Detail & Related papers  (2020-05-11T04:00:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.