Anomaly Detection for Agricultural Vehicles Using Autoencoders

Mujkic, Esma; Philipsen, Mark Philip; Moeslund, Thomas B.; Christiansen, Martin Peter; Ravn, Ole

doi:10.3390/s22103608

Cited by 21 publications

(3 citation statements)

References 32 publications

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“…To compare different autoencoder models for real-time anomaly detection, Mujkic et al [27] evaluated the following three models: the denoising autoencoder (DAE) [28], semisupervised autoencoder (SSAE), and variational autoencoder (VQ-VAE) [29], against the baseline YOLOv5 [30]. Although YOLOv5 slightly outperformed SSAE in the AUROC score (0.945 vs. 0.8849), SSAE demonstrated better performance in critical cases.…”

Section: Reconstruction-based Anomaly Localizationmentioning

confidence: 99%

Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control

Mehta,

Klarmann

2023

MAKE

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Manufacturing industries require the efficient and voluminous production of high-quality finished goods. In the context of Industry 4.0, visual anomaly detection poses an optimistic solution for automatically controlled product quality with high precision. In general, automation based on computer vision is a promising solution to prevent bottlenecks at the product quality checkpoint. We considered recent advancements in machine learning to improve visual defect localization, but challenges persist in obtaining a balanced feature set and database of the wide variety of defects occurring in the production line. Hence, this paper proposes a defect localizing autoencoder with unsupervised class selection by clustering with k-means the features extracted from a pretrained VGG16 network. Moreover, the selected classes of defects are augmented with natural wild textures to simulate artificial defects. The study demonstrates the effectiveness of the defect localizing autoencoder with unsupervised class selection for improving defect detection in manufacturing industries. The proposed methodology shows promising results with precise and accurate localization of quality defects on melamine-faced boards for the furniture industry. Incorporating artificial defects into the training data shows significant potential for practical implementation in real-world quality control scenarios.

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Section: Reconstruction-based Anomaly Localizationmentioning

confidence: 99%

Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control

Mehta,

Klarmann

2023

MAKE

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“…A study found that they can detect anomalies in agricultural vehicles by identifying obstructive objects in the field. The study also introduced a semi-supervised autoencoder trained with a max-margin-inspired loss function that outperformed basic and denoising autoencoders in the generation of anomaly maps using relative-perceptual–L1 loss [ 18 ]. According to the research, autoencoders are identified as a versatile tool for anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

TimeTector: A Twin-Branch Approach for Unsupervised Anomaly Detection in Livestock Sensor Noisy Data (TT-TBAD)

Kakar,

Hussain,

Kim

et al. 2024

Sensors

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Unsupervised anomaly detection in multivariate time series sensor data is a complex task with diverse applications in different domains such as livestock farming and agriculture (LF&A), the Internet of Things (IoT), and human activity recognition (HAR). Advanced machine learning techniques are necessary to detect multi-sensor time series data anomalies. The primary focus of this research is to develop state-of-the-art machine learning methods for detecting anomalies in multi-sensor data. Time series sensors frequently produce multi-sensor data with anomalies, which makes it difficult to establish standard patterns that can capture spatial and temporal correlations. Our innovative approach enables the accurate identification of normal, abnormal, and noisy patterns, thus minimizing the risk of misinterpreting models when dealing with mixed noisy data during training. This can potentially result in the model deriving incorrect conclusions. To address these challenges, we propose a novel approach called “TimeTector-Twin-Branch Shared LSTM Autoencoder” which incorporates several Multi-Head Attention mechanisms. Additionally, our system now incorporates the Twin-Branch method which facilitates the simultaneous execution of multiple tasks, such as data reconstruction and prediction error, allowing for efficient multi-task learning. We also compare our proposed model to several benchmark anomaly detection models using our dataset, and the results show less error (MSE, MAE, and RMSE) in reconstruction and higher accuracy scores (precision, recall, and F1) against the baseline models, demonstrating that our approach outperforms these existing models.

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“…The paper presents a framework for combining the detection of multiple scene understanding tasks. The proposed ensemble method is an extension of the author’s previous work on semantic segmentation ( Mujkic et al., 2020 ), anomaly detection and object detection ( Mujkic et al., 2022 ). Deep-learning based-algorithms for semantic segmentation, object detection and anomaly detection are trained individually.…”

Section: Introductionmentioning

confidence: 99%

Framework for environment perception: Ensemble method for vision-based scene understanding algorithms in agriculture

2023

Self Cite

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The safe and reliable operation of autonomous agricultural vehicles requires an advanced environment perception system. An important component of perception systems is vision-based algorithms for detecting objects and other structures in the fields. This paper presents an ensemble method for combining outputs of three scene understanding tasks: semantic segmentation, object detection and anomaly detection in the agricultural context. The proposed framework uses an object detector to detect seven agriculture-specific classes. The anomaly detector detects all other objects that do not belong to these classes. In addition, the segmentation map of the field is utilized to provide additional information if the objects are located inside or outside the field area. The detections of different algorithms are combined at inference time, and the proposed ensemble method is independent of underlying algorithms. The results show that combining object detection with anomaly detection can increase the number of detected objects in agricultural scene images.

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Anomaly Detection for Agricultural Vehicles Using Autoencoders

Cited by 21 publications

References 32 publications

Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control

Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control

TimeTector: A Twin-Branch Approach for Unsupervised Anomaly Detection in Livestock Sensor Noisy Data (TT-TBAD)

Framework for environment perception: Ensemble method for vision-based scene understanding algorithms in agriculture

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