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Hyperspectral Imaging for Identifying Foreign Objects on Pork Belly

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arXiv 2025年

作者： Ghimpeteanu, Gabriela Rajani, Hayat Quintana, Josep Garcia, Rafael Coronis Computing SL University of Girona Science and Technology Park Carrer Pic de Peguera 11 Edifici Giroemprèn Girona17003 Spain Computer Vision and Robotics Research Institute University of Girona Campus Montilivi Edifici P4 Girona17003 Spain

Ensuring food safety and quality is critical in the food processing industry, where the detection of contaminants remains a persistent challenge. This study presents an automated solution for detecting foreign objects on pork belly meat using hyperspectral imaging (HSI). A hyperspectral camera was used to capture data across various bands in the near-infrared (NIR) spectrum (900-1700 nm), enabling accurate identification of contaminants that are often undetectable through traditional visual inspection methods. The proposed solution combines pre-processing techniques with a segmentation approach based on a lightweight vision Transformer (ViT) to distinguish contaminants from meat, fat, and conveyor belt materials. The adopted strategy demonstrates high detection accuracy and training efficiency, while also addressing key industrial challenges such as inherent noise, temperature variations, and spectral similarity between contaminants and pork belly. Experimental results validate the effectiveness of hyperspectral imaging in enhancing food safety, highlighting its potential for broad real-time applications in automated quality control processes. © 2025, CC BY-NC-SA.

关键词： Belt conveyors

Constricting Normal Latent Space for Anomaly Detection with Normal-only Training Data

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arXiv 2024年

作者： Astrid, Marcella Zaheer, Muhammad Zaigham Lee, Seung-Ik Department of Artificial Intelligence University of Science and Technology Korea Republic of Field Robotics Research Section Electronics and Telecommunications Research Institute Korea Republic of Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg Department of Computer Vision Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates

In order to devise an anomaly detection model using only normal training data, an autoencoder (AE) is typically trained to reconstruct the data. As a result, the AE can extract normal representations in its latent space. During test time, since AE is not trained using real anomalies, it is expected to poorly reconstruct the anomalous data. However, several researchers have observed that it is not the case. In this work, we propose to limit the reconstruction capability of AE by introducing a novel latent constriction loss, which is added to the existing reconstruction loss. By using our method, no extra computational cost is added to the AE during test time. Evaluations using three video anomaly detection benchmark datasets, i.e., Ped2, Avenue, and ShanghaiTech, demonstrate the effectiveness of our method in limiting the reconstruction capability of AE, which leads to a better anomaly detection model. © 2024, CC BY-NC-SA.

关键词： Anomaly detection

Towards a Unified Approach to Homography Estimation Using Image Features and Pixel Intensities

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arXiv 2022年

作者： Nogueira, Lucas de Paiva, Ely C. Silveira, Geraldo School of Mechanical Engineering University of Campinas Campinas SP Brazil Robotics and Computer Vision Research Group Center for Information Technology Renato Archer Campinas SP Brazil

The homography matrix is a key component in various vision-based robotic tasks. Traditionally, homography estimation algorithms are classified into feature- or intensity-based. The main advantages of the latter are their versatility, accuracy, and robustness to arbitrary illumination changes. On the other hand, they have a smaller domain of convergence than the feature-based solutions. Their combination is hence promising, but existing techniques only apply them sequentially. This paper proposes a new hybrid method that unifies both classes into a single nonlinear optimization procedure, applies the same minimization method, and uses the same homography parametrization and warping function. Experimental validation using a classical testing framework shows that the proposed unified approach has improved convergence properties compared to each individual class. These are also demonstrated in a visual tracking application. As a final contribution, our ready-to-use implementation of the algorithm is made publicly available to the research community. © 2022, CC BY.

关键词： computer vision

FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition

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arXiv 2023年

作者： Elbatel, Marawan Martí, Robert Li, Xiaomeng The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The Computer Vision and Robotics Institute University of Girona Spain The Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

Representational transfer from publicly available models is a promising technique for improving medical image classification, especially in long-tailed datasets with rare diseases. However, existing methods often overlook the frequency-dependent behavior of these models, thereby limiting their effectiveness in transferring representations and generalizations to rare diseases. In this paper, we propose FoPro-KD, a novel framework that leverages the power of frequency patterns learned from frozen pretrained models to enhance their transferability and compression, presenting a few unique insights: 1) We demonstrate that leveraging representations from publicly available pre-trained models can substantially improve performance, specifically for rare classes, even when utilizing representations from a smaller pre-trained model. 2) We observe that pre-trained models exhibit frequency preferences, which we explore using our proposed Fourier Prompt Generator (FPG), allowing us to manipulate specific frequencies in the input image, enhancing the discriminative representational transfer. 3) By amplifying or diminishing these frequencies in the input image, we enable Effective Knowledge Distillation (EKD). EKD facilitates the transfer of knowledge from pre-trained models to smaller models. Through extensive experiments in long-tailed gastrointestinal image recognition and skin lesion classification, where rare diseases are prevalent, our FoPro-KD framework outperforms existing methods, enabling more accessible medical models for rare disease classification. Code is available at https://***/xmed-lab/FoPro-KD. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

*** 1.0: The Full Autonomous Stack for Oval Racing at High Speeds

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arXiv 2023年

作者： Raji, Ayoub Caporale, Danilo Gatti, Francesco Giove, Andrea Verucchi, Micaela Malatesta, Davide Musiu, Nicola Toschi, Alessandro Popitanu, Silviu Roberto Bagni, Fabio Bosi, Massimiliano Liniger, Alexander Bertogna, Marko Morra, Daniele Amerotti, Francesco Bartoli, Luca Martello, Federico Porta, Riccardo University of Modena and Reggio Emilia Italy University of Parma Italy Technology Innovation Institute - Autonomous Robotics Research Center United Arab Emirates HIPERT srl Italy University of Pisa Italy Computer Vision Lab ETH Zurich Switzerland

The Indy Autonomous Challenge (IAC) brought together for the first time in history nine autonomous racing teams competing at unprecedented speed and in head-to-head scenario, using independently developed software on open-wheel racecars. This paper presents the complete software architecture used by team TII EuroRacing (TII-ER), covering all the modules needed to avoid static obstacles, perform active overtakes and reach speeds above 75 m/s (270 km/h). In addition to the most common modules related to perception, planning, and control, we discuss the approaches used for vehicle dynamics modelling, simulation, telemetry, and safety. Overall results and the performance of each module are described, as well as the lessons learned during the first two events of the competition on oval tracks, where the team placed respectively second and third. Copyright © 2023, The Authors. All rights reserved.

关键词： Racing automobiles

Edge-guided Representation Learning for Underwater Object Detection

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arXiv 2023年

作者： Dai, Linhui Liu, Hong Song, Pinhao Tang, Hao Ding, Runwei Li, Shengquan Key Laboratory of Machine Perception Shenzhen Graduate School Peking University Shenzhen China Robotics Research Group KU Leuven Leuven Belgium Computer Vision Lab ETH Zurich Zurich Switzerland Peng Cheng Laboratory Shenzhen China

Underwater object detection (UOD) is crucial for marine economic development, environmental protection, and the planet’s sustainable development. The main challenges of this task arise from low-contrast, small objects, and mimicry of aquatic organisms. The key to addressing these challenges is to focus the model on obtaining more discriminative information. We observe that the edges of underwater objects are highly unique and can be distinguished from low-contrast or mimicry environments based on their edges. Motivated by this observation, we propose an Edge-guided Representation Learning Network, termed ERL-Net, that aims to achieve discriminative representation learning and aggregation under the guidance of edge cues. Firstly, we introduce an edge-guided attention module to model the explicit boundary information, which generates more discriminative features. Secondly, a feature aggregation module is proposed to aggregate the multi-scale discriminative features by regrouping them into three levels, effectively aggregating global and local information for locating and recognizing underwater objects. Finally, we propose a wide and asymmetric receptive field block to enable features to have a wider receptive field, allowing the model to focus on more small object information. Comprehensive experiments on three challenging underwater datasets show that our method achieves superior performance on the UOD task. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

Deep Learning vs. Traditional 3d Registration: A Featureless 3d Registration Baseline

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SSRN

SSRN 2023年

作者： Bojanic, David Bartol, Kristijan Forest, Josep Petkovic, Tomislav Pribanic, Tomislav University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb10000 Croatia TU Dresden Dresden01069 Germany University of Girona Computer Vision and Robotics Research Institute Plaça de Sant Domènec 3 Girona17004 Spain

Recent 3D registration methods are mostly learning-based that either find correspondences in feature space and match them, or directly estimate the registration transformation from the given point cloud features. Therefore, these feature based methods have difficulties with generalizing onto point clouds that differ substantially from their training data. We propose a simple featureless traditional 3D registration baseline method based on the weighted cross-correlation between two given point clouds. Our method achieves strong results on current benchmarking datasets, outperforming most deep-learning methods. We further propose a methodology to create a 3D registration benchmark, given a point cloud dataset, that provides a more informative evaluation of a method w.r.t. other benchmarks. We also propose to use the 3D registration parameter range of a benchmark as an indicator of its difficulty. Using this methodology, we create a novel FAUST-partial (FP) benchmark, based on the FAUST dataset, with several difficulty levels. The FP benchmark addresses the limitations of the current benchmarks: lack of data and parameter range variability, and allows to evaluate the strengths and weaknesses of a 3D registration method w.r.t. a single registration parameter. Using the new FP benchmark, we provide a thorough analysis of the current state-of-the-art methods. Our source code is available on GitHub. © 2023, The Authors. All rights reserved.

关键词： Deep learning

Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies

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arXiv 2024年

作者： Astrid, Marcella Zaheer, Muhammad Zaigham Aouada, Djamila Lee, Seung-Ik Department of Artificial Intelligence University of Science and Technology Daejeon34113 Korea Republic of Field Robotics Research Section Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg1855 Luxembourg Department of Computer Vision Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal training data. At test time, the trained AE is expected to well reconstruct normal but to poorly reconstruct anomalous data. However, contrary to the expectation, anomalous data is often well reconstructed as well. In order to further separate the reconstruction quality between normal and anomalous data, we propose creating pseudo anomalies from learned adaptive noise by exploiting the aforementioned weakness of AE, i.e., reconstructing anomalies too well. The generated noise is added to the normal data to create pseudo anomalies. Extensive experiments on Ped2, Avenue, ShanghaiTech, CIFAR-10, and KDDCUP datasets demonstrate the effectiveness and generic applicability of our approach in improving the discriminative capability of AEs for anomaly detection. © 2024, CC BY-NC-SA.

关键词： Anomaly detection

Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast Learning

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arXiv 2023年

作者： Cao, Yihong Zhang, Hui Lu, Xiao Xiao, Zheng Yang, Kailun Wang, Yaonan College of Computer Science and Electronic Engineering Hunan University Changsha410082 China National Engineering Research Center of Robot Vision Perception and Control Technology School of Robotics Hunan University Changsha410082 China College of Engineering and Design Hunan Normal University Changsha410082 China

Domain adaptive semantic segmentation enables robust pixel-wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and storage limitations in typical unsupervised domain adaptation methods, making it especially relevant in the context of intelligent vehicles. It utilizes a well-trained source model and unlabeled target data to achieve adaptation in the target domain. However, in the absence of source data and target labels, current solutions cannot sufficiently reduce the impact of domain shift and fully leverage the information from the target data. In this paper, we propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast (IAPC) learning. The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain. Specifically, considering the problem of domain shift in the prediction of the target domain by the source model, we put forward an importance-aware mechanism for the biased target prediction probability distribution to extract domain-invariant knowledge from the source model. We further introduce a prototype-contrast strategy, which includes a prototype-symmetric cross-entropy loss and a prototype-enhanced cross-entropy loss, to learn target intra-domain knowledge without relying on labels. A comprehensive variety of experiments on two domain adaptive semantic segmentation benchmarks demonstrates that the proposed end-to-end IAPC solution outperforms existing state-of-the-art methods. The source code is publicly available at https://***/yihong-97/Source-free-IAPC. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics