The 19th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2024

Inhaltsverzeichnis

1. Frontmatter

2. Special Session: Machine Learning and Computer Vision in Industry 4.0

   1. Frontmatter

   2. Computer Vision Based Quality Control for Molding Injection Machines

      Ramón Moreno, Oscar García, Miguel Del Río Cristobal, Revanth Shankar Muthuselvam, José María Sanjuan, Andrés Vallejo, Ting Wang

      Abstract

      This paper presents a comprehensive study on the application of computer vision, specifically Convolutional Neural Networks (CNNs), for quality control in automotive interior manufacturing using Injection Molding Machines (IMMs). Despite the high precision and productivity of IMMs, a small percentage of defects persist. Addressing this, the paper compares four popular CNN algorithms—ResNet-50, SegNet, FCN, and PSPNet—on their ability to detect and analyze defects in post-manufacturing. The study outlines the advantages of IMMs, the challenges in defect management, and the potential of CNNs to revolutionize quality control processes. Experimental results demonstrate the efficacy of these algorithms, paving the way for enhanced quality management and reduced defect rates in the automotive sector.

   3. Towards a Comprehensive Taxonomy of Cobots: A Tool for Multi-criteria Classification

      Michael Fernández Vega, David Alfaro Víquez, Mauricio-Andres Zamora-Hernandez, Jose Garcia-Rodriguez, Jorge Azorín-López

      Abstract

      This paper presents a proposed taxonomy for collaborative robotics, encompassing a detailed classification based on various technical and functional characteristics applicable to robotic equipment in the context of collaborative tasks. The proposed taxonomy includes thirteen categories and arises from the need for an initial reference framework for the analysis and classification of robots, allowing not only the identification of yet uncovered research topics but also serving as a starting point for classifying different types of current robots. This will help determine whether they can be used in shared spaces with people or not, given that today there are robots with unique characteristics that make it difficult to ascertain their suitability for use in shared spaces. This is due to the fine line between what can be considered a collaborative robot and one for general or industrial use.

   4. Novel Positional Encoding Methods for Neural Rendering

      Daniel Molina-Pinel, Jorge García-González, Enrique Domínguez, Ezequiel López-Rubio

      Abstract

      This work focuses on proposing alternatives for the \(\gamma ^\varphi \) function used to perform positional encoding in volumetric rendering methods based on radiance field optimization. The approach is to replace the standard sinusoidal function with other proposed alternatives having similar properties. Alternatives that combine several \(\gamma ^\varphi \) functions are also tested. Experiments with quantitative and qualitative results are included to evaluate the results.

   5. Enhancing Object Segmentation via Few-Shot Learning with Limited Annotated Data

      Iván García-Aguilar, Syed Ali Haider Jafri, David Elizondo, Saul Calderón, Sarah Greenfield, Rafael M. Luque-Baena

      Abstract

      Significant advancements in machine learning in recent years have revolutionized multiple sectors. The Segment-Anything Model (SAM) is a notable example of state-of-the-art image segmentation. Despite claims of zero-shot generalization, SAM exhibits limitations in specific scenarios like medical mammography images. SAM generates three segmentation masks per image to address this and recommends selecting the one with the highest confidence score. However, this is not always the optimal choice. This paper introduces a system that extends SAM’s segmentation capabilities by automatically selecting the correct mask, leveraging few-shot learning methods and an Out-of-Distribution threshold strategy. Several backbones were subjected to experimentation, highlighting the relationship between the support set size and the model’s accuracy.

   6. Early Breast Cancer Detection by Automated Analysis of Mammograms with Deep Convolutional Networks

      Guillermo Tell-González, Ezequiel López-Rubio, Rafaela Benítez-Rochel, Miguel A. Molina-Cabello

      Abstract

      Breast cancer is one of the most significant issues today. Doctors use methods like mammograms for its detection. Early recognition of this disease is crucial, so automation using convolutional neural networks is proposed, classifying mammograms according to BI-RADS categories. The proposed methodology seeks to streamline image analysis through the automation capabilities of convolutional neuronal networks, presenting a swifter and more reliable alternative to manual assessment. Multiple class distributions were conducted on the dataset to enhance the model’s performance. These divisions provided a deeper understanding of the model and facilitated the identification of optimal parameter combinations and strategies to optimize its effectiveness. Experimental findings affirm the method’s proficiency in accurately classifying mammography images according to BI-RADS standards. This innovative approach holds promise for the development of automated systems to aid radiologists in the early detection of breast cancer.

   7. Feature Selection for Multi-label Classification in Predictive Maintenance

      Antoine Hubermont, Aymeric Vellinger, Nemanja Antonic, Elio Tuci

      Abstract

      In this study, we propose a novel feature selection technique to improve the classification of simultaneous anomalies in the context of predictive maintenance. We leverage three publicly available datasets collected from measurements and characterised by high dimensions. In this context, the use of machine learning algorithms with 5 multi-label classifiers has proven effective in anomaly classification tasks. Therefore, we implement a self-adaptive evolutionary strategy algorithm tasked with selecting the most relevant features. We assess the cost and the effectiveness of the classification task with and without feature selection. Overall, our results show that the self-adaptive evolutionary strategy is able to drastically reduce the number of features required for training, while improving or maintaining the performance of the classification task. Our study underscores the potential of feature selection for predictive maintenance multi-label classification tasks, contributing to more efficient and effective predictive maintenance strategies in industrial settings.

   8. Transforming Manufacturing Through Human Digital Twins: A New Architectural Approach

      David Alfaro-Viquez, Mauricio-Andres Zamora-Hernandez, Michael-Alejandro Fernandez-Vega, Jose Garcia-Rodriguez, Jorge Azorín-López

      Abstract

      This study presents a novel architecture for Human Digital Twins (HDT) applied in manufacturing, designed to address several challenges in the current industry, including data management, safety, intelligent assistance, and operator training. We analysed various existing proposals, highlighting the need for adaptable and scalable architectures that support the complexity of industrial processes. Our proposed architecture integrates and processes multimodal data to facilitate the incorporation of human related information, as emotion detection, fatigue identification, and intelligent monitoring, among others. These technologies that are embedded in our proposal are crucial for improving operational efficiency and safety in the workplace. The proposed architecture stands out for its ability to adapt to various industrial applications, making it a viable solution for implementing digital twins in cyber-physical environments.

   9. From Medical Consultation to Diagnosis Generation Using Deep Learning

      David Ortiz-Perez, Alejandro Merino-Balaguer, Javier Rodriguez-Juan, Jose Garcia-Rodriguez, David Tomás, Grzegorz J. Nalepa

      Abstract

      Sanitary professionals often experience burnout due to long working hours and the stress associated with handling sensitive situations. Consequently, in recent years, significant efforts have been made to automate various tasks within their roles using deep learning methods. This study proposes an automated system for generating summaries of medical consultations, aiming to alleviate doctors’ workloads. The architecture takes audio recordings of medical consultations as input, which are then transcribed and diarized for further processing. Subsequently, the entire transcription is summarized using state-of-the-art transformer-based models. For this study, a dataset comprising fourteen consultations, along with two summaries provided by different sanitary professionals, was utilized. In order to address the limitations of the available data, a data augmentation technique was implemented which makes use of the capabilities of large language models. The proposed models achieved a result of 0.52 in the ROGUE-1 metric, demonstrating the effectiveness of the proposed methodology.

   10. Advancing Brick Detection from Lab to Industry: A Machine Vision Approach for Robotic Applications

       Dominik Stursa, Petr Dolezel, Bruno B. Zanon

       Abstract

       This paper focuses on the application of a computer vision system for the detection of interlacing bricks within a robotic handling system, highlighting the challenges and solutions related to transitioning these systems from controlled laboratory environments to dynamic industrial manufacturing conditions. By addressing practical problems encountered in industrial settings, we emphasize the reliability issues of image-based object detection systems when integrated into automated robotic manipulation processes. The study includes the development and testing of various modified U-Net architectures for brick detection and evaluates their performance in both laboratory and industrial environments.

   11. Computer-Vision-Based Industrial Algorithm for Detecting Fruit and Vegetable Dimensions and Positioning

       Cesar Guevara, Julen Rostan, Javier Rodriguez, Silvia Gonzalez, Javier Sedano

       Abstract

       A computer-vision-based industrial algorithm is proposed in this study for the detection of the dimensions and the spatial positioning of fruit and vegetables on a conveyor belt for their movement to a packing machine with a robotic arm. The principal purpose of the algorithm is to identify the dimensions (length, width), and position (angle of inclination, and Cartesian coordinates) of the vegetable mesh netting without taking the product label into account. The proposed algorithm has four functions. A convolutive neuronal network model is applied for object detection, with which all objects are identified in the image while the product label is suppressed, so that recognition of the vegetable mesh netting and its dimensions is not impaired. Moreover, the Canny edge detector and the Border following algorithms are applied to perform image pre-processing and edge improvements, respectively, yielding optimal results with more clearly defined noise-free objects. The feature extraction function for dimensions yielded detection precision results of 99.60% for length, 99.00% for width, and 97.80% for inclination angle, showing optimal performance in the test phases.