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Revolutionizing Robotic Depalletizing: AI-Enhanced Parcel Detecting with Adaptive 3D Machine Vision and RGB-D Imaging for Automated Unloading

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SENSORS 2024年第5期24卷 1473页

作者： Kim, Seongje Truong, Van-Doi Lee, Kwang-Hee Yoon, Jonghun Hanyang Univ Dept Mech Design Engn 222 Wangsimni Ro Seoul 04763 South Korea Hanyang Univ BK21 FOUR ERICA ACE Ctr 55 Hanyangdaehak-ro Ansan 15588 South Korea Korea Inst Ind Technol KITECH Ansan 15588 South Korea Hanyang Univ Dept Mech Engn 55Hanyangdaehak Ro Ansan 15588 South Korea AIDICOME Inc 55Hanyangdaehak Ro Ansan 15588 South Korea

Detecting parcels accurately and efficiently has always been a challenging task when unloading from trucks onto conveyor belts because of the diverse and complex ways in which parcels are stacked. Conventional methods struggle to quickly and accurately classify the various shapes and surface patterns of unordered parcels. In this paper, we propose a parcel-picking surface detection method based on deep learning and image processing for the efficient unloading of diverse and unordered parcels. Our goal is to develop a systematic image processing algorithm that emphasises the boundaries of parcels regardless of their shape, pattern, or layout. The core of the algorithm is the utilisation of RGB-D technology for detecting the primary boundary lines regardless of obstacles such as adhesive labels, tapes, or parcel surface patterns. For cases where detecting the boundary lines is difficult owing to narrow gaps between parcels, we propose using deep learning-based boundary line detection through the You Only Look at Coefficients (YOLACT) model. Using image segmentation techniques, the algorithm efficiently predicts boundary lines, enabling the accurate detection of irregularly sized parcels with complex surface patterns. Furthermore, even for rotated parcels, we can extract their edges through complex mathematical operations using the depth values of the specified position, enabling the detection of the wider surfaces of the rotated parcels. Finally, we validate the accuracy and real-time performance of our proposed method through various case studies, achieving mAP (50) values of 93.8% and 90.8% for randomly sized and rotationally covered boxes with diverse colours and patterns, respectively.

关键词： parcel detection unloading system RGB-D machine vision object detection image segmentation

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An adaptable physics-informed fault diagnosis approach via hybrid signal processing and transferable feature learning for structural/machinery health monitoring

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SIGNAL image AND VIDEO processing 2024年第12期18卷 9051-9066页

作者： Zarchi, Milad Shahgholi, Majid Tee, Kong Fah Shahid Rajaee Teacher Training Univ Fac Mech Engn Tehran *** Iran King Fahd Univ Petr & Minerals Dept Civil & Environm Engn Dhahran 31261 Saudi Arabia KFUPM Interdisciplinary Res Ctr Construct & Bldg Mat Dhahran 31261 Saudi Arabia

Structural damages, such as structural looseness and structural cracks, are commonly observed as the root causes of failures in industrial plants. These issues have been extensively studied, and deep diagnostic tools have shown promise in identifying and addressing them. However, these tools rely on large amounts of data, which leads to computational burdens and time consumption. To tackle this challenge, a groundbreaking technique is proposed within the context of this study. The key innovation of this approach lies in its ability to integrate information from various processing functions and utilize an efficient feature bank that facilitates the execution of an effective feature learning method based on a multisource strategy. This novel research also focuses on the selection of transferable features from multiple distributions for diagnostics involving unseen failure distributions. By minimizing the mean squared error function, which is based on various source domains, the accuracy of diagnostics is significantly improved. Furthermore, the joint minimization of diagnostics independence concerning failure distribution, as well as the dimension of the transferable feature space between the source domains, leads to enhanced diagnostics speed and feature visualization. To validate the effectiveness of this approach, a real case study of a structural/machinery vibration dataset is conducted to address the multi-fault diagnosis problem, encompassing machinery health conditions, foundation looseness, and cracks under various operational conditions. The results obtained from this study demonstrate that the proposed algorithm performs remarkably well in real diagnostics scenarios involving unseen failure distributions.

关键词： Structural/machinery health monitoring Multidomain data analysis Multisource information fusion Multiprocessing module Transfer learning Adaptable feature extraction

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timely ICU Outcome Prediction Utilizing Stochastic Signal Analysis and Machine learning Techniques with Readily Available Vital Sign Data

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第9期28卷 5587-5599页

作者： Wang, Shaodong Jiang, Yiqun Li, Qing Zhang, Wenli Iowa State Univ Dept Ind & Mfg Syst Engn Ames IA 50011 USA Iowa State Univ Dept Informat Syst & Business Analyt Ames IA 50011 USA

The ICU is a specialized hospital department that offers critical care to patients at high risk. The massive burden of ICU-requiring care requires accurate and timely ICU outcome predictions for alleviating the economic and healthcare burdens imposed by critical care needs. Existing research faces challenges such as feature extraction difficulties, low accuracy, and resource-intensive features. Some studies have explored deep learning models that utilize raw clinical inputs. However, these models are considered non-interpretable black boxes, which prevents their wide application. The objective of the study is to develop a new method using stochastic signal analysis and machine learning techniques to effectively extract features with strong predictive power from ICU patients' real-time time series of vital signs for accurate and timely ICU outcome prediction. The results show the proposed method extracted meaningful features and outperforms baseline methods, including APACHE IV (AUC = 0.750), deep learning-based models (AUC = 0.732, 0.712, 0.698, 0.722), and statistical feature classification methods (AUC = 0.765) by a large margin (AUC = 0.869). The proposed method has clinical, management, and administrative implications since it enables healthcare professionals to identify deviations from prognostications timely and accurately and, therefore, to conduct proper interventions.

关键词： Predictive models Feature extraction Stochastic processes real-time systems Data models Accuracy E-health records machine learning medical information systems patient monitoring prediction methods signal processing signal processing

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Combining Synthetic images and deep Active learning: Data-Efficient Training of an Industrial Object Detection Model

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JOURNAL OF IMAGING 2024年第1期10卷 16-16页

作者： Eversberg, Leon Lambrecht, Jens Wang, Guanghui Tech Univ Berlin Fac Electr Engn & Comp Sci 4 Ind Grade Networks & Clouds Str 17 Juni 135 D-10623 Berlin Germany

Generating synthetic data is a promising solution to the challenge of limited training data for industrial deep learning applications. However, training on synthetic data and testing on real-world data creates a sim-to-real domain gap. Research has shown that the combination of synthetic and real images leads to better results than those that are generated using only one source of data. In this work, the generation of synthetic training images via physics-based rendering is combined with deep active learning for an industrial object detection task to iteratively improve model performance over time. Our experimental results show that synthetic images improve model performance, especially at the beginning of the model's life cycle with limited training data. Furthermore, our implemented hybrid query strategy selects diverse and informative new training images in each active learning cycle, which outperforms random sampling. In conclusion, this work presents a workflow to train and iteratively improve object detection models with a small number of real-world images, leading to data-efficient and cost-effective computer vision models.

关键词： active learning computer vision data efficiency deep active learning deep learning image synthesis industrial application object detection synthetic images turbine blade

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real-time Sign Language Fingerspelling Recognition System Using 2D deep CNN with Two-Stream Feature Extraction Approach

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第9期15卷 1062-1072页

作者： Zhidebayeva, Aziza Nurmukhanbetova, Gulira Aldeshov, Sapargali Zhamalova, Kamshat Mamikov, Satmyrza Torebay, Nursaule Univ Friendship Peoples Academician A Kuatbekov Shymkent Kazakhstan South Kazakhstan State Pedag Univ Shymkent Kazakhstan Miras Univ Shymkent Kazakhstan

This research paper introduces a novel sign language recognition system developed using advanced deep learning (DL) techniques aimed at enhancing communication capabilities between deaf and hearing individuals. The system leverages a convolutional neural network (CNN) architecture, optimized for the real-time interpretation of dynamic hand gestures that constitute sign language. A comprehensive dataset was employed to train and validate the model, encompassing a diverse range of gestures across different environmental settings. Comparative analysis revealed that the deep learning-based model significantly outperforms traditional machine learning techniques in terms of recognition accuracy, particularly with the increase in the volume of training data. This was illustrated through various performance metrics, including a detailed confusion matrix and Levenshtein distance measurements, highlighting the system's efficacy in accurately identifying complex gestures. real-time application tests further demonstrated the model's robustness and adaptability to varying lighting conditions and backgrounds, essential for practical deployment. Key challenges identified include the need for broader linguistic diversity in training datasets and enhanced model sensitivity tosubtle gestural distinctions. The paper concludes with suggestions for future research directions, emphasizing algorithm optimization, data diversification, and user-centric design improvements to foster wider adoption and usability. This study underscores the potential of deep learning technologies to revolutionize assistive communication tools, making them more accessible and effective for the deaf community.

关键词： deep learning sign language recognition convolutional neural networks real-time processing gesture recognition machine learning accessibility technology

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Generative AI-based style recommendation using fashion item detection and classification

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SIGNAL image AND VIDEO processing 2024年第12期18卷 9179-9189页

作者： Kalinin, Aleksandr Jafari, Akbar Anbar Avots, Egils Ozcinar, Cagri Anbarjafari, Gholamreza Univ Tartu iCV Lab Tartu Estonia 3S Holding OU Uus Veeriku Tee 1 EE-62220 Tartu Estonia PwC Advisory Helsinki Finland iVCV OU Purpuri 12-2 EE-51011 Tartu Estonia

This research work focuses on the creation of a cutting-edge style recommendation system that uses generative AI and deep learning approaches to analyse fashion photos. The system is intended to process input images, such as selfies or studio-quality photos, and output a text file with extensive feedback on the individual's style and suggestions for improvement. The system consists of two main components: the YOLOv8 convolutional neural network trained on deepFashion2 dataset, which detects and crops clothing items, and the GPT-4.0 large language model, which generates informative style commentary and recommendations. YOLOv8 is briefly trained on a specific dataset to improve its performance in recognising 10 different types of clothes, while GPT-4.0, which is accessible via the OpenAI API, is charged with giving cohesive and short style suggestions. To evaluate the success of the suggested solution, real experimental trials were conducted at many events in Madrid and Tallinn. Three well-known AI models were used for comparison: OpenAI's GPT-4.0 Vision, Google's Gemini 1.5 Pro as reported by Reid et al. (Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context, 2024), and Anthropic's Claude 3 (Templeton et al. in Tansform Circuits Thread, 2024)-Opus. Participants judged the quality of each model's fashion recommendations. The results showed that GPT-4.0 Vision and Gemini 1.5 Pro had comparable average ratings, indicating higher perceived quality than Claude 3-Opus. This research work demonstrates how cutting-edge computer vision and natural language processing technology may transform personalised fashion advising services, improving accuracy and relevance of style recommendations.

关键词： Generative AI deep learning Object detection image processing Fashion

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High-speed system-on-chip-based platform for real-time crop disease and pest detection using deep learning techniques

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Mallick, Tausif Murty, D. Omkar Pal, Ranita Mandal, Swagata Saha, Himadri Nath Chakrabarti, Amlan Univ Calcutta AKCSIT Kolkata India Neotia Univ Dept Comp Sci & Engn AI & ML SST Sarisha India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata India Jalpaiguri Govt Engn Coll Dept Elect & Commun Kolkata India Univ Calcutta Dept Comp Sci SNEC Kolkata India

Crop diseases significantly threaten global agricultural productivity and food security, leading to economic losses and increased pesticide use, which pollutes soil and water and disrupts ecological balance. Mustard and mung bean crops are particularly affected by various diseases and pests such as Alternaria blight, aphids, charcoal rot, bruchids, and mosaic. timely and accurately identifying these diseases and pests are crucial for effective crop management. This research tackles disease classification in mustard and mung bean crops by employing transfer learning, a MobileNetV3based CNN model, and a System-on-Chip (SoC) computing platform. The processing system and processing logic of SoC enhance computing flexibility. Xilinx deep learning Processor Unit (DPU) intellectual property (IP) accelerates disease classification 24 times compared to software counterparts. At the same time, our proposed design enhances the throughput by around 29% and reduces the power consumption by around 19%. MobileNetV3 achieves classification accuracies of 96.14% on mung bean and 93.25% on mustard datasets, surpassing other state-of-the-art methods. A vital aspect of this research is developing a user-friendly mobile application for image capture, communication with SoC, and result display, making disease and pest detection more convenient and accessible. The SoC-based system is versatile and can be extended to classify various crop varieties beyond mung bean and mustard without hardware modifications.

关键词： Plant disease detection Plant pest detection CNN Transfer learning image classification System on chip

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Fast real-time Brain Tumor Detection Based on Stimulated Raman Histology and Self-Supervised deep learning Model

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JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024年第3期37卷 1160-1176页

作者： Wang, Zijun Han, Kaitai Liu, Wu Wang, Zhenghui Shi, Chaojing Liu, Xi Huang, Mengyuan Sun, Guocheng Liu, Shitou Guo, Qianjin Beijing Inst Petrochem Technol Acad Artificial Intelligence Beijing 102617 Peoples R China

In intraoperative brain cancer procedures, real-time diagnosis is essential for ensuring safe and effective care. The prevailing workflow, which relies on histological staining with hematoxylin and eosin (H&E) for tissue processing, is resource-intensive, time-consuming, and requires considerable labor. Recently, an innovative approach combining stimulated Raman histology (SRH) and deep convolutional neural networks (CNN) has emerged, creating a new avenue for real-time cancer diagnosis during surgery. While this approach exhibits potential, there exists an opportunity for refinement in the domain of feature extraction. In this study, we employ coherent Raman scattering imaging method and a self-supervised deep learning model (VQVAE2) to enhance the speed of SRH image acquisition and feature representation, thereby enhancing the capability of automated real-time bedside diagnosis. Specifically, we propose the VQSRS network, which integrates vector quantization with a proxy task based on patch annotation for analysis of brain tumor subtypes. Training on images collected from the SRS microscopy system, our VQSRS demonstrates a significant speed enhancement over traditional techniques (e.g., 20-30 min). Comparative studies in dimensionality reduction clustering confirm the diagnostic capacity of VQSRS rivals that of CNN. By learning a hierarchical structure of recognizable histological features, VQSRS classifies major tissue pathological categories in brain tumors. Additionally, an external semantic segmentation method is applied for identifying tumor-infiltrated regions in SRH images. Collectively, these findings indicate that this automated real-time prediction technique holds the potential to streamline intraoperative cancer diagnosis, providing assistance to pathologists in simplifying the process.

关键词： Stimulated Raman histology SRS imaging system Brain tumor subtype classification Intraoperative diagnosis Vector quantization Generative deep learning

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Space Imaging Point Source Detection and Characterization

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IEEE ACCESS 2024年 12卷 90442-90460页

作者： Ribeiro, Francisco S. F. Garcia, Paulo J. V. Silva, Miguel Cardoso, Jaime S. Univ Porto Fac Engn P-4250465 Porto Portugal Inst Super Tecn CENTRA P-1049001 Lisbon Portugal INESC TEC P-4200465 Porto Portugal

Point source detection algorithms play a pivotal role across diverse applications, influencing fields such as astronomy, biomedical imaging, environmental monitoring, and beyond. This article reviews the algorithms used for space imaging applications from ground and space telescopes. The main difficulties in detection arise from the incomplete knowledge of the impulse function of the imaging system, which depends on the aperture, atmospheric turbulence (for ground-based telescopes), and other factors, some of which are time-dependent. Incomplete knowledge of the impulse function decreases the effectiveness of the algorithms. In recent years, deep learning techniques have been employed to mitigate this problem and have the potential to outperform more traditional approaches. The success of deep learning techniques in object detection has been observed in many fields, and recent developments can further improve the accuracy. However, deep learning methods are still in the early stages of adoption and are used less frequently than traditional approaches. In this review, we discuss the main challenges of point source detection, as well as the latest developments, covering both traditional and current deep learning methods. In addition, we present a comparison between the two approaches to better demonstrate the advantages of each methodology.

关键词： Noise measurement Detectors deep learning Apertures Signal to noise ratio Atmospheric measurements image processing Machine learning Object detection image annotation digital images high-resolution imaging machine learning object detection

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A real-time intelligent monitoring method for indoor evacuee distribution based on deep learning and spatial division

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JOURNAL OF BUILDING ENGINEERING 2024年 92卷

作者： Han, Litao Feng, Hengjian Liu, Guoyu Zhang, Aiguo Han, Tao Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao Peoples R China Shandong Univ Sci & Technol Key Lab Geomat & Digital Technol Shandong Prov Qingdao Peoples R China Xiamen Univ Technol Coll Comp & Informat Engn Xiamen Peoples R China

Providing real-time and detailed indoor evacuee distribution information is of great significance for the on-site formulating and dynamic adjustment of indoor emergency evacuation strategies. However, due to limitations in hardware costs and privacy protection, existing video surveillance and other evacuee counting methods cannot achieve real-time and accurate monitoring of the number and distribution of evacuees in all indoor areas. To solve the problem, using video data from an indoor surveillance system as a data source, an intelligent monitoring method for indoor pedestrian real-time distribution based on deep learning and spatial division is proposed. Firstly, the whole indoor area is divided many subareas according to the layout of indoor space and the size of spatial units. Then, deep learning detection and tracking algorithms are used to detect and track evacuees to achieve the number of evacuees at the boundary of each subarea and their movement directions;Finally, the numbers of evacuees entering and leaving each subarea are counted to obtain the spatial distribution information of evacuees. The experimental results show that within an appropriate monitoring distance, the proposed method achieves an average F1 score of 91.85% for evacuee counting at the boundaries of each subarea, with a processing speed of 22 FPS. More comprehensive and accurate real-time monitoring of evacuee distribution in the entire indoor space can be achieved, including no covered areas of cameras. It not only helps to formulate on-site emergency evacuation in case of a fire, but also enhances the daily operation and management capabilities of buildings.

关键词： Intelligent monitoring Spatial division Evacuee distribution Building evacuation Computer vision

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