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Effectiveness Analysis of Entrepreneurial Legal Risk Prevention Based on Multi-Modal deep learning Model

ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION processing 2024年第6期23卷 1-16页

作者： Li, Tianhua Qu, Shaowei Univ Sci & Technol Beijing Inst Educ Econ & Management 30 Xueyuan Rd Beijing 100083 Peoples R China

With the emergence of the upsurge of entrepreneurship, entrepreneurs are increasingly concerned about legal risks. In the process of entrepreneurship, legal risk is the biggest hidden danger of entrepreneurial en-terprises. The prevention and avoidance of legal risks is a long and arduous process, and not all risks can be identified and avoided in time. The deep learning method has brought great changes to the fields of speech recognition, image recognition and natural language processing. The tasks in these fields only involve single-mode input, but more recent applications need to involve multi-mode intelligence. Multimodal deep learning mainly includes three aspects: multimodal learning representation, multimodal signal fusion and multimodal application. Through the research on the legal risks in recent years, this paper believed that there are many legal problems in the development of entrepreneurial enterprises, including contract disputes, trade secret disputes, trademark infringement disputes, copyright infringement disputes, and so on. This paper aimed to study the problem of Legal Risks of Entrepreneurship (LRE) that entrepreneurs are concerned about, and proposed a new solution from the perspective of image recognition technology. 486 entrepreneurs were inves-tigated by questionnaire. In the process of entrepreneurship, 87.04% of entrepreneurs encountered legal risks, and they would turn to lawyers for help, which is a better way. However, in most cases, entrepreneurs are exposed to LRE because of their insufficient understanding of economic law, so they solve it through lawyers. However, if a lawyer is hired, the cost would be very high, which would bring great economic pressure to the enterprise. Only 17.90% of entrepreneurs would safeguard their legitimate rights and interests through their own knowledge and legal weapons without resorting to lawyers. It can be seen that entrepreneurs have relatively low practical ability in the use of LRE, and their le

关键词： Venture Legal Risk image recognition technology risk prevention arti-ficial neural network multimodal deep learning model

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A Low-Cost Vegetable Quality Assessment System Based on Microscopy images in deep learning Edge Computing: A Pilot Study on Potato Tuber

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第3期70卷 6343-6353页

作者： Biswas, Sumona Barma, Shovan Indian Inst Informat Technol Guwahati Dept Elect & Commun Engn Gauhati 713103 India

This work details design and development of a microscopy image-based vegetable quality assessment system (Prototype) by adopting deep learning (DL) technique on edge device. Current automated machine learning methods primarily utilize outer-surface images of vegetables/fruits, often lacking in precise quantification of nutrient content such as carbohydrates, minerals, vitamins, etc. Indeed, such nutrient ingredients can be assessed by examining micro-level cell attributes of microscopy images in DL framework. However, vegetable quality detection based on microscopy/DLs on resource-constrained edge devices poses significant challenges. To address these problems, a portable, cost-effective, efficient, and real-time prototype has been realized. It involves configuring a microscopy image generation module using low-cost Foldscope lens coupled with smartphones and on-device analysis by designing a new lightweight DL architecture and segmentation algorithm. The analysis is executed via a smartphone application, ensuring advantages like bandwidth and energy efficiency, user privacy, local processing without external servers. For system validation, a pilot study has been conducted on the widely consumed potato tuber, focusing on the assessment of starch presence as a key quality metric. The system successfully assesses cell attributes, i.e., starch quantity of 10-25% in similar to 24s, which is very much consistent. In a comparative study, the network outperforms the existing state-of-the-art lightweight networks by achieving the highest recognition accuracy upto 88.8% and F1-score 85.83 with lesser parameters (1.5M) and FLOPs (118M). Thus, the study demonstrates its applicability for vegetable quality assessment in an easy, affordable, and effective way. Further, the proposed idea can be extended to other vegetables/fruits.

关键词： deep learning edge computing Foldscope lightweight network potato smartphone vegetable quality

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Single-frame noisy interferogram phase retrieval using an end-to-end deep learning network with physical information constraints

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OPTICS AND LASERS IN ENGINEERING 2024年 181卷

作者： Zhang, Tian Shi, Runzhou Shao, Yuqi Chen, Qijie Bai, Jian Zhejiang Univ Coll Opt Sci & Engn Hangzhou 310027 Peoples R China Zhejiang Univ ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou 311215 Peoples R China Zhejiang Univ State Key Lab Extreme Photon & Instrumentat Hangzhou 310027 Peoples R China

Phase retrieval from a single-frame interferogram is a meaningful approach in dynamic and real-time interferometry. However, existing demodulation methods are susceptible to real noise impressions, resulting in low accuracy. This paper proposes an accurate end-to-end learning-based method for one-shot phase retrieval that doesn't require pre-processing (e.g., denoising, normalization) and post-processing (e.g., phase unwrapping). Compared to existing deep learning methods, we incorporate more physical information constraints, contributing to high accuracy and robustness. A realistic noise model that incorporates the speckle noise and the imaging process is introduced to generate training datasets. Furthermore, a subnetwork is adopted for noise estimation, along with a subnetwork for frequency domain decomposition to extract multi-channel physical information. Additionally, a physical information fusion block is established to integrate relevant data, and a reconstruction network based on NAFNet is implemented for comprehensive end-to-end phase retrieval. We validate the accuracy and robustness of our approach through simulations and experiments. Our results demonstrate that the proposed method achieves a phase retrieval accuracy of 0.01 lambda for noisy interferograms, superior to several existing methods that we have reproduced.

关键词： Interferometry Phase retrieval deep learning

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Laceration assessment: advanced segmentation and classification framework for retinal disease categorization in optical coherence tomography images

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JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS image SCIENCE AND VISION 2024年第9期41卷 1786-1793页

作者： Mani, Pavithra Ramachandran, Neelaveni Paul, Sweety Jose Ramesh, Prasanna Venkatesh Kongu Engn Coll Dept ECE Perundurai Tamil Nadu India PSG Coll Technol Dept EEE Coimbatore Tamil Nadu India Mahathma Eye Hosp Pvt Ltd Dept Glaucoma & Res Tiruchirappalli Tamil Nadu India

Disorders affecting the retina pose a considerable risk to human vision, with an array of factors including aging, diabetes, hypertension, obesity, ocular trauma, and tobacco use exacerbating this issue in contemporary times. Optical coherence tomography (OCT) is a rapidly developing imaging modality that is capable of identifying early signs of vascular, ocular, and central nervous system abnormalities. OCT can diagnose retinal diseases through image classification, but quantifying the laceration area requires image segmentation. To overcome this obstacle, we have developed an innovative deep learning framework that can perform both tasks simultaneously. The suggested framework employs a parallel mask-guided convolutional neural network (PM-CNN) for the classification of OCT B-scans and a grade activation map (GAM) output from the PM-CNN to help a V-Net network (GAM V-Net) to segment retinal lacerations. The guiding mask for the PM-CNN is obtained from the auxiliary segmentation job. The effectiveness of the dual framework was evaluated using a combined dataset that encompassed four publicly accessible datasets along with an additional real-time dataset. This compilation included 11 categories of retinal diseases. The four publicly available datasets provided a robust foundation for the validation of the dual framework, while the real-time dataset enabled the framework's performance to be assessed on a broader range of retinal disease categories. The segmentation Dice coefficient was 78.33 +/- 0.15%, while the classification accuracy was 99.10 +/- 0.10%. The model's ability to effectively segment retinal fluids and identify retinal lacerations on a different dataset was an excellent demonstration of its generalizability. (c) 2024 Optica Publishing Group

关键词： Biomedical imaging deep learning Imaging techniques Machine learning Neural networks Optical coherence tomography

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Industrial object detection with multi-modal SSD: closing the gap between synthetic and real images

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MULtimeDIA TOOLS AND APPLICATIONS 2024年第4期83卷 12111-12138页

作者： Cohen, Julia Crispim-Junior, Carlos Chiappa, Jean-Marc Rodet, Laure Tougne Univ Lyon Univ Lyon 2 CNRS Cent LyonINSA LyonUCBLLIRISUMR 5205 F-69676 Bron France DEMS St Bonnet de Mure France

Object detection for industrial applications faces challenges that are yet to solve by state-of-the-art deep learning models. They usually lack training data, and the common solution of using a synthetic dataset introduces a domain gap when the model is provided real images. Besides, few architectures fit in the small memory of a mobile device and run in real-time with limited computation capabilities. The models fulfilling these requirements generally have low learning capacity, and the domain gap reduces further the performance. In this work, we propose multiple strategies to reduce the domain gap when using RGB-D images, and to increase the overall performance of a Convolutional Neural Network (CNN) for object detection with a reasonable increase of the model size. First, we propose a new architecture based on the Single Shot Detector (SSD) architecture, and we compare different fusion methods to increase the performance with few or no additional parameters. We applied the proposed method to three synthetic datasets with different visual characteristics, and we show that classical image processing reduces significantly the domain gap for depth maps. Our experiments have shown an improvement when fusing RGB and depth images for two benchmark datasets, even when the depth maps contain few discriminative information. Our RGB-D SSD Lite model performs on par or better than a ResNet-FPN RetinaNet model on the LINEMOD and T-LESS datasets, while requiring 20 times less computation. Finally, we provide some insights on training a robust model for improved performance when one of the modalities is missing.

关键词： Object detection deep learning Synthetic dataset Industrial RGB-D

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real-time video anomaly detection for smart surveillance

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IET image processing 2023年第5期17卷 1375-1388页

作者： Ali, Manal Mostafa Al Azhar Univ Comp & Syst Engn Cairo 11751 Egypt

Human monitoring of surveillance cameras for anomaly detection may be a monotonous task as it requires constant attention to judge if the captured activities are anomalous or suspicious. This paper exploits background subtraction (BS), convolutional autoencoder, and object detection for a fully automated surveillance system. BS was performed by modelling each pixel as a mixture of Gaussians (MoG) to concatenate only the higher-order learning in the foreground. Next, the foreground objects are fed to the convolutional autoencoders to filter out abnormal events from normal ones and automatically identify signs of threat and violence in real time. Then, object detection is introduced on the entire scene and the region of interest is highlighted with a bounding box to minimize human intervention in video stream processing. At recognition time, the network generates an alarm for the presence of an anomaly to notify of the identification of potentially suspicious actions. Finally, the complete system is validated upon several benchmark datasets and proved to be robust for complex video anomaly detection. The (AUC) average area under the curve for the frame-level evaluation for all benchmarks is 94.94%. The best improvement ratio of AUC between the proposed system and state-of-the-art methods is 7.7%.

关键词： anomaly detection background subtraction computer vision deep learning real-time surveillance

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deep learning-based robust positioning scheme for imaging sonar guided dynamic docking of autonomous underwater vehicle

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OCEAN ENGINEERING 2024年 293卷

作者： Wang, Zhao Xiang, Xianbo Guan, Xiawei Pan, Han Yang, Shaolong Chen, Hong Huazhong Univ Sci & Technol Sch Naval Architecture & Ocean Engn 1037 Luoyu Rd Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China Wuhan Second Ship Design & Res Inst Wuhan 430074 Peoples R China Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut Shanghai 200240 Peoples R China

In this paper, a deep learning -based underwater positioning scheme is proposed to achieve robust feature tracking of an autonomous underwater vehicle (AUV) in sonar image during dynamic docking. To address the issues that the distorted feature and acoustic noises lead significant difficulty to detection and tracking of AUV in acoustic image during dynamic docking, first, a pre -trained You Only Look Once (YOLO) network is applied to detect both body and head features of AUV. Second, we introduce an Intersection Over Union (IOU) match -based backend which preliminarily filters the error detections of AUV head based on the rigid relationship between body and head of AUV. Subsequently, Simple Online and realtime Tracking with a deep association metric (deepSort) is utilized to achieve track matching of all detection results including error detections and real target. Moreover, a scoring mechanism is presented to further remove the unfiltered error detections based on the motion tendency of detection tracks. Experiment result shows that the proposed scheme enables real-time and robust feature tracking of AUV with the interference of feature distortion, reverberation and environmental noises.

关键词： Autonomous underwater vehicles (AUVs) Dynamic docking deep learning Acoustic image Underwater positioning

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Research on Water Gauge Recognition Based on deep learning and image processing 4

Research on Water Gauge Recognition Based on Deep Learning a...

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4th International Conference on E-Business, Information Management and Computer Science, EBIMCS 2021

作者： Tang, Yuehao Guo, Chong Changjiang Institute Of Technology School Of Water Conservancy And Electric Power Wuhan China Wuhan Business University Research Center For Chinese And Western Language And Culture Wuhan China

ISBN: (纸本)9781450395687

In order to improve the intellective level of water resources management, a real-time water level recognition method based on deep-learning algorithms and image-processing techniques is proposed in this paper. The recognition process is composed of four steps. Firstly, for the purpose of digit detection, YOLO-v3 model is deployed for extracting numbers from the water gauges. Then, the cropped number images are fed into the LSTM + CTC model as training samples so that digits can be recognized. In the third step, Hough transform are adopted to correct the tilt of water gauge in terms of the vertical edge feature. Morphological operation, associated with horizontal projection would position upper and lower edge of water gauge to recognize the scale lines correctly. Water level could be determined correspondingly. Model application shows that the recognition model has satisfying accuracy and efficiency, with potential being applied in practice. © 2021 ACM.

关键词： Water levels

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Hybrid deep learning Approach for Enhanced Animal Breed Classification and Prediction

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TRAITEMENT DU SIGNAL 2023年第5期40卷 2087-2099页

作者： Khan, Safdar Sardar Doohan, Nitika Vats Gupta, Manish Jaffari, Sakina Chourasia, Ankita Joshi, Kriti Panchal, Bhupendra RKDF Univ Dept Comp Sci & Engn Bhopal 462033 India Medi Caps Univ Dept Comp Sci & Engn Indore 453331 India GLA Univ Dept Elect & Commun Engn Mathura 281406 India VIT Bhopal Univ Sch Comp Sci & Engn Sehore 466114 India

The precise classification of animal breeds from image data is instrumental in real-time animal monitoring within forest ecosystems. Traditional computer vision methods have increasingly fallen short in accuracy due to the rapid progression of technology. To address these limitations, more advanced methodologies have emerged, significantly improving the accuracy of image classification, recognition, and segmentation tasks. The advent of "deep learning" has revolutionized various fields, particularly in object identification and recognition. Animal breed categorization is an important job in the field of image processing, and this research attempts to create a unique deep learning-based model for this purpose. The aim of this research is to devise efficient methodologies for image-based animal breed categorization to achieve superior accuracy levels. A hybrid deep learning model is proposed for animal breed prediction. The animal-10 dataset, obtained from Kaggle, serves as the empirical foundation for this study. The dataset underwent preprocessing, including edge deletion, normalization, and image scaling. Additionally, the animal images were converted into grayscale. Following this preprocessing phase, feature extraction was performed using two deep learning methods, namely VGG-19 and DenseNet121. The performance metrics, including accuracy, F1 score, recall, precision, and loss, were computed for the developed model using the Python simulation tool. Experimental results indicate that the proposed model outperforms existing current models in terms of these metrics. This research outcomes hold promising implications for the advancement of animal breed classification and prediction techniques.

关键词： animal breed prediction image processing classification deep learning VGG-19, densenet-121

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A Review of Advanced deep learning Methods of Multi-Target Segmentation for Breast Cancer WSIs

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IEEE ACCESS 2025年 13卷 76016-76037页

作者： Xu, Qiaoyi Adam, Afzan Abdullah, Azizi Bariyah, Nurkhairul Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia Zibo Vocat Inst Coll Intelligent Mfg Zibo 255314 Peoples R China Univ Kebangsaan Malaysia Fac Med Dept Pathol Kuala Lumpur 56000 Malaysia

Breast cancer is one of the most common cancers among women, with its heterogeneity posing significant challenges for diagnosis and treatment, profoundly impacting patient prognosis and quality of life. Whole Slide Imaging (WSI) in digital pathology provides high-resolution images that enable a comprehensive examination of the tumor microenvironment, offering advanced tools for breast cancer diagnosis and prognostic evaluation. However, manually reviewing whole slide images (WSIs) for tissue segmentation is time-consuming and prone to errors, highlighting the need for multi-target deep learning models to automate the segmentation of these complex structures. Multi-target segmentation offers distinct advantages by simultaneously processing multiple interrelated tissue regions within a single image, thereby enhancing accuracy and efficiency. Despite the potential of deep learning techniques in automating pathological analysis, their clinical adoption faces significant challenges. To address these, this paper proposes six criteria focused on clinical acceptability of deep learning methods: inherent limitations of WSIs, feature extraction, annotation requirements, efficiency, automated quantification, and interpretability. A rigorous review of publicly available datasets and deep learning methods identifies key challenges for clinical adoption. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review analyzes 29 core articles, highlighting the critical role of multi-target segmentation in breast cancer digital pathology while assessing the limitations of these techniques in clinical applications. Based on this analysis, this paper proposes six criteria to enhance the diagnostic performance of deep learning methods in multi-target segmentation for breast cancer digital pathology and to improve the clinical acceptability of deep learning methods.

关键词： Annotations Breast cancer Pathology deep learning image segmentation Cancer Accuracy image resolution Feature extraction XML multi-target segmentation digital pathology whole slide images segmentation algorithms deep learning

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