Anomaly-Detection on ViCoS Lab

Anomalous Sound Detection by Feature-Level Anomaly Simulation

Mon, 01 Jan 0001 00:00:00 +0000

Recently a growing number of works focus on machine defect detection from anomalous audio patterns. The datasets for the machine audio domain are scarce and recent methods that perform well on benchmarks such as DCASE2020 Task 2, rely on auxiliary information such as annotated data from other training classes in the domain to extract information that can be used in deep-learning classification-based anomaly detection approaches. However, in practical scenarios, annotated data from the same domain may not be readily available so annotation-free methods that can learn appropriate audio representations from unannotated data are needed. We propose AudDSR, a simulation-based anomaly detection method that learns to detect anomalies without additional annotated data and instead focuses on a discrete feature space sampling method for an anomaly simulation process. AudDSR outperforms competing methods that do not rely on annotated data on the DCASE2020 anomalous sound detection benchmark and even matches the performance of some methods that utilize additional annotation information.

Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation

Mon, 01 Jan 0001 00:00:00 +0000

RGB-based surface anomaly detection methods have advanced significantly. However, certain surface anomalies remain practically invisible in RGB alone, necessitating the incorporation of 3D information. Existing approaches that employ point-cloud backbones suffer from suboptimal representations and reduced applicability due to slow processing. Re-training RGB backbones, designed for faster dense input processing, on industrial depth datasets is hindered by the limited availability of sufficiently large datasets. We make several contributions to address these challenges. (i) We propose a novel Depth-Aware Discrete Autoencoder (DADA) architecture, that enables learning a general discrete latent space that jointly models RGB and 3D data for 3D surface anomaly detection. (ii) We tackle the lack of diverse industrial depth datasets by introducing a simulation process for learning informative depth features in the depth encoder. (iii) We propose a new surface anomaly detection method 3DSR, which outperforms all existing state-of-the-art on the challenging MVTec3D anomaly detection benchmark, both in terms of accuracy and processing speed. The experimental results validate the effectiveness and efficiency of our approach, highlighting the potential of utilizing depth information for improved surface anomaly detection.

DSR – A Dual Subspace Re-Projection Network for Surface Anomaly Detection

Mon, 01 Jan 0001 00:00:00 +0000

The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images. Such approaches are prone to failure on near-in-distribution anomalies since these are difficult to be synthesized realistically due to their similarity to anomaly-free regions. We propose an architecture based on quantized feature space representation with dual decoders, DSR, that avoids the image-level anomaly synthesis requirement. Without making any assumptions about the visual properties of anomalies, DSR generates the anomalies at the feature level by sampling the learned quantized feature space, which allows a controlled generation of near-in-distribution anomalies. DSR achieves state-of-the-art results on the KSDD2 and MVTec anomaly detection datasets. The experiments on the challenging real-world KSDD2 dataset show that DSR significantly outperforms other unsupervised surface anomaly detection methods, improving the previous top-performing methods by 10% AP in anomaly detection and 35% AP in anomaly localization.

Keep DRÆMing: Discriminative 3D anomaly detection through anomaly simulation

Mon, 01 Jan 0001 00:00:00 +0000

Recent surface anomaly detection methods rely on pretrained backbone networks for efficient anomaly detection. On standard RGB anomaly detection benchmarks these methods achieve excellent results but fail on 3D anomaly detection due to a lack of pretrained backbones that suit this domain. Additionally, there is a lack of industrial depth data that would enable the backbone network training that could be used in 3D anomaly detection models. Discriminative anomaly detection methods do not require pretrained networks and are trained using simulated anomalies. The process of simulating anomalies that fit the domain of industrial depth data is not trivial and is necessary for training discriminative methods. We propose a novel 3D anomaly simulation process that follows the natural characteristics of industrial depth data and generates diverse deformations, making it suitable for training discriminative anomaly detection methods. We demonstrate its effectiveness by adapting the DRÆM method to work on 3D anomaly detection, thus obtaining 3DRÆM, a strong discriminative 3D anomaly detection model. The proposed approach achieves excellent results on the MVTec3D anomaly detection benchmark where it achieves state-of-the-art results on both 3D and RGB+3D problem setups, significantly outperforming competing methods.

Multi-modal Obstacle Avoidance in USVs via Anomaly Detection and Cascaded Datasets

Mon, 01 Jan 0001 00:00:00 +0000

We introduce a novel strategy for obstacle avoidance in aqua- tic settings, using anomaly detection for quick deployment of autonomous water vehicles in limited geographic areas. The unmanned surface vehi- cle (USV) is initially manually navigated to collect training data. The learning phase involves three steps: learning imaging modality specifics, learning the obstacle-free environment using collected data, and setting obstacle detector sensitivity with images containing water obstacles. This approach, which we call cascaded datasets, works with different image modalities and environments without extensive marine-specific data. Re- sults are demonstrated with LWIR and RGB images from river missions.