Continuity-Driven Representation Learning for Industrial Defect Detection

• URL: http://arxiv.org/abs/2608.17362v1
• Date: Tue, 18 Aug 2026 04:31:55 GMT
• Title: Continuity-Driven Representation Learning for Industrial Defect Detection
• Abstract summary: Industrial defect detection differs from natural-image object detection because inspection images contain large normal-dominant regions with repetitive structures.<n>We propose a continuity-driven representation regularization framework that exploits normal-dominant regions as dense auxiliary supervision.
• Score: 10.126273003671388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Industrial defect detection differs from natural-image object detection because inspection images are captured under controlled conditions and contain large normal-dominant regions with repetitive structures. Defects therefore appear as localized disruptions of otherwise predictable patterns, while conventional detectors rely mainly on sparse bounding-box supervision, resulting in weakly constrained normal-region representations. We propose a continuity-driven representation regularization framework that exploits normal-dominant regions as dense auxiliary supervision. The framework introduces two detector-agnostic objectives: Multi-Continuity Loss, which combines 1D patch-sequence prediction and 2D masked spatial prediction, and Differencing Loss, which regularizes first-order feature variation and second-order curvature between neighboring patch embeddings. Both objectives are applied with box-derived region weighting to stabilize normal-region representations while preserving defect-related discontinuities. Experiments on two real-world industrial datasets and the public NEU-DET benchmark, using six detector architectures including YOLO-family models, MambaYOLO, and DETR, demonstrate consistent improvements over native detector baselines. In the full-data setting, the proposed regularizers improve average mAP@0.5:0.95 by up to 3.49 percentage points on Industrial Metal, 5.38 percentage points on MEA, and 5.03 percentage points on NEU-DET. Under limited-data conditions, the gains become more pronounced, with Differencing Loss achieving improvements of up to 21.07 percentage points in mAP@0.5 and 8.23 percentage points in mAP@0.5:0.95 on NEU-DET using only 25% of the training data. These results suggest that continuity-driven regularization provides an effective prior for improving industrial defect detection, particularly when annotated data are scarce.

Related papers

• Spatial Attention Supervision for Defect Localization: Exploiting Ground-Truth Masks as Training Signal in Diffusion-Augmented Defect Detection [0.0]
  Ground-truth defect masks in industrial datasets are typically reserved for evaluation.<n>This paper repurposes them as spatial supervision signals during training of classification networks.
  arXiv  Detail & Related papers  (2026-09-05T19:20:23Z)
• GeoID-PINN: Identifiability-Aware Regional Epidemic Inference with Geographic Coupling [1.0875426095207128]
  We introduce a physics-informed neural network (PINN) for susceptible-infectious-recovered-deceased dynamics.<n>We regularize a spatial prior constructed from distance, adjacency, commuting, or lead-lag information.<n>Relative to an autoregressive negative-binomial baseline, Forecast-Trained Geo-PINN reduces mean squared error (MSE) from 32,957 to 11,468 and mean absolute error (MAE) from 70.60 to 57.73.
  arXiv  Detail & Related papers  (2026-07-28T05:59:23Z)
• Rough Path Signature-Guided Geometry Augmentation for Few-Shot Industrial Surface Defect Detection [0.0]
  This paper proposes rough path signature-guided geometry augmentation (RPS-GA)<n>RPS-GA is a geometry-aware approach in which Canny edge contours are treated as ordered planar paths.<n>Results indicate that second-order path-signature geometry offers a practical way to strengthen few-shot industrial defect detection.
  arXiv  Detail & Related papers  (2026-07-14T01:14:58Z)
• FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry [52.232826445242644]
  FoundationGeo bridges relative and metric prediction via spatial calibration and principled data design.<n>Stage 1 learns a high-fidelity, affine-invariant geometry model by initializing with DINOv3.<n>Stage 2 moves beyond global scaling by introducing pixel-wise calibration fields for metric estimation.
  arXiv  Detail & Related papers  (2026-07-13T14:10:01Z)
• IAD-Unify: A Region-Grounded Unified Model for Industrial Anomaly Segmentation, Understanding, and Generation [21.900464211494022]
  Real-world industrial inspection requires localizing defects, explaining them in natural language and generating controlled defect edits.<n>We propose IAD-Unify, a dual-encoder unified framework in which a frozen DINOv2-based region expert supplies precise anomaly evidence.<n>IAD-Unify achieves strong performance on the MMAD benchmark, including categories unseen during training, demonstrating robust cross-category generalization.
  arXiv  Detail & Related papers  (2026-04-14T08:29:31Z)
• Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection [50.343419243749054]
  Anomaly detection is critical in fields such as medical diagnostics and industrial defect detection.<n> CLIP's coarse-grained image-text alignment limits localization and detection performance for fine-grained anomalies.<n>Crane improves the state-of-the-art ZSAD from 2% to 28%, at both image and pixel levels, while remaining competitive in inference speed.
  arXiv  Detail & Related papers  (2025-04-15T10:42:25Z)
• Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift [51.24522135151649]
  Anomaly detection plays a crucial role in quality control for industrial applications.<n>Existing methods attempt to address domain shifts by training generalizable models.<n>Our proposed method demonstrates superior results compared with state-of-the-art anomaly detection and domain adaptation methods.
  arXiv  Detail & Related papers  (2025-03-19T05:25:52Z)
• Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection [20.651257973799527]
  Recalibrating Attention of Industrial Anomaly Detection (RAAD) is a framework that systematically decomposes and recalibrates attention maps.<n> HQS dynamically adjusts bit-widths based on the hierarchical nature of attention maps.<n>We validate the effectiveness of RAAD on 32 datasets using a single 3090ti.
  arXiv  Detail & Related papers  (2024-12-11T08:31:47Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• REB: Reducing Biases in Representation for Industrial Anomaly Detection [16.550844182346314]
  We propose Reducing Biases (REB) in representation by considering the domain bias and building a self-supervised learning task for better domain adaption. We also propose a local-density KNN (LDKNN) to reduce the local density bias in the feature space and obtain effective anomaly detection. The proposed REB method achieves a promising result of 99.5% Im.AUROC on the widely used MVTec AD, with smaller backbone networks such as Vgg11 and Resnet18.
  arXiv  Detail & Related papers  (2023-08-24T05:32:29Z)
• Exploring the Relationship between Samples and Masks for Robust Defect Localization [1.90365714903665]
  This paper proposes a one-stage framework that detects defective patterns directly without the modeling process. Explicit information that could indicate the position of defects is intentionally excluded to avoid learning any direct mapping. Results show that the proposed method is 2.9% higher than the SOTA methods in F1-Score, while substantially outperforming SOTA methods in generalizability.
  arXiv  Detail & Related papers  (2023-06-19T06:41:19Z)
• Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers [44.344548601242444]
  We introduce a novel framework, Weakly-supervised RESidual Transformer (WeakREST), to achieve high anomaly detection accuracy.<n>We reformulate the pixel-wise anomaly localization task into a block-wise classification problem.<n>We develop a novel ResMixMatch algorithm, capable of handling the interplay between weak labels and residual-based representations.
  arXiv  Detail & Related papers  (2023-06-06T08:19:30Z)
• PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization [64.39761523935613]
  We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding. It also exploits correlations between the different semantic levels of CNN to better localize anomalies.
  arXiv  Detail & Related papers  (2020-11-17T17:29:18Z)
• Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection [85.53263670166304]
  One-stage detector basically formulates object detection as dense classification and localization. Recent trend for one-stage detectors is to introduce an individual prediction branch to estimate the quality of localization. This paper delves into the representations of the above three fundamental elements: quality estimation, classification and localization.
  arXiv  Detail & Related papers  (2020-06-08T07:24:33Z)

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.