CRD: Collaborative Representation Distance for Practical Anomaly Detection
Visual defect detection plays an important role in intelligent industry. Patch based methods consider visual images as a collection of image patches according to positions, which have stronger discriminative ability for small defects in products, e.g. scratches on pills. However, the nearest neighbor search for the query image and the stored patches will occupy $O(n)$ complexity in terms of time and space requirements, posing strict challenges for deployment in edge environments. In this paper, we propose an alternative approach to the distance calculation of image patches via collaborative representation models. Starting from the nearest neighbor distance with $L_0$ constraint, we relax the constraint to $L_2$ constraint and solve the distance quickly in close-formed without actually accessing the original stored collection of image patches. Furthermore, we point out that the main computational burden of this close-formed solution can be pre-computed by high-performance server before deployment. Consequently, the distance calculation on edge devices only requires a simple matrix multiplication, which is extremely lightweight and GPU-friendly. Performance on real industrial scenarios demonstrates that compared to the existing state-of-the-art methods, this distance achieves several hundred times improvement in computational efficiency with slight performance drop, while greatly reducing memory overhead.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionComputational EfficiencyDefect DetectionGPUSimilar Papers 제목 키워드 기반
Federated Anomaly Detection for Multi-Tenant Cloud Platforms with Personalized Modeling
This paper proposes an anomaly detection method based on federated learning to address key challenges in multi-tenant cloud environments, including data privacy leakage, heterogeneous resource behavior, and the limitatio…
Federated LearningAnomaly DetectionBridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation
3D point cloud anomaly detection is essential for robust vision systems but is challenged by pose variations and complex geometric anomalies. Existing patch-based methods often suffer from geometric fidelity issues due t…
3D Anomaly DetectionAnomaly DetectionAnomaly LocalizationNonnegative-Constrained Joint Collaborative Representation with Union Dictionary for Hyperspectral Anomaly Detection
Recently, many collaborative representation-based (CR) algorithms have been proposed for hyperspectral anomaly detection. CR-based detectors approximate the image by a linear combination of background dictionaries and th…
Anomaly Detection3D Human-Human Interaction Anomaly Detection
Human-centric anomaly detection (AD) has been primarily studied to specify anomalous behaviors in a single person. However, as humans by nature tend to act in a collaborative manner, behavioral anomalies can also arise f…
Anomaly DetectionDMAD: Dual Memory Bank for Real-World Anomaly Detection
Training a unified model is considered to be more suitable for practical industrial anomaly detection scenarios due to its generalization ability and storage efficiency. However, this multi-class setting, which exclusive…
Anomaly DetectionRepresentation Learning