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YOLO and residual network for colorectal cancer cell detection and counting

HELIYON 2024年第2期10卷 e24403页

作者： Haq, Inayatul Mazhar, Tehseen Asif, Rizwana Naz Ghadi, Yazeed Yasin Ullah, Najib Khan, Muhammad Amir Al-Rasheed, Amal Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Virtual Univ Pakistan Dept Comp Sci Lahore 55150 Pakistan Natl Coll Business Adm & Econ Sch Comp Sci Lahore 54000 Pakistan Al Ain Univ Dept Software Engn & Comp Sci Abu Dhabi 12555 U Arab Emirates Univ Balochistan Fac Pharm & Hlth Sci Dept Pharm Quetta 08770 Pakistan Univ Teknol MARA Coll Comp Informat & Math Sch Comp Sci Shah Alam 40450 Selangor Malaysia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia

The HT-29 cell line, derived from human colon cancer, is valuable for biological and cancer research applications. Early detection is crucial for improving the chances of survival, and researchers are introducing new techniques for accurate cancer diagnosis. This study introduces an efficient deep learning-based method for detecting and counting colorectal cancer cells (HT-29). The colorectal cancer cell line was procured from a company. Further, the cancer cells were cultured, and a transwell experiment was conducted in the lab to collect the dataset of colorectal cancer cell images via fluorescence microscopy. Of the 566 images, 80 % were allocated to the training set, and the remaining 20 % were assigned to the testing set. The HT-29 cell detection and counting in medical images is performed by integrating YOLOv2, ResNet-50, and ResNet-18 architectures. The accuracy achieved by ResNet-18 is 98.70 % and ResNet-50 is 96.66 %. The study achieves its primary objective by focusing on detecting and quantifying congested and overlapping colorectal cancer cells within the images. This innovative work constitutes a significant development in overlapping cancer cell detection and counting, paving the way for novel advancements and opening new avenues for research and clinical applications. Researchers can extend the study by exploring variations in ResNet and YOLO architectures to optimize object detection performance. Further investigation into real-time deployment strategies will enhance the practical applicability of these models.

关键词： Biomedical image processing Computer-aided diagnosis Medical image analysis Machine learning deep learning HT-29 cells Colon cancer

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A deep learning framework for face verification without alignment

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JOURNAL OF real-time image processing 2021年第4期18卷 999-1009页

作者： Fan, Zhongkui Guan, Ye-peng Shanghai Univ Sch Commun & Informat Engn Shanghai Peoples R China Minist Educ Key Lab Adv Displays & Syst Applicat Shanghai Peoples R China

Most of the CNN (convolutional neural networks) methods require alignment, which will affect the efficiency of verification. This paper proposes a deep face verification framework without alignment. First and foremost, the framework consists of two training stages and one testing stage. In the first training stage, the CNN is fully trained on the large face dataset. In the second training stage, embedding triplet is adopted to fine-tune the models. Furthermore, in the testing stage, SIFT (scale invariant feature transform) descriptors are extracted from intermediate pooling results for cascading verification, which effectively improves the accuracy of face verification without alignment. Last but not least, two CNN architectures are designed for different scenarios. The CNN1 (convolutional neural networks 1), with fewer layers and parameters, requires a small amount of memory and computation in training and testing, so it is suitable for real-time system. The CNN2 (convolutional neural networks 2), with more layers and parameters, has excellent face verification. Through the long-term training on WEB-face dataset and experiments on the LFW (labled faces in the wild), YTB (YouTube) datasets, the results show that the proposed method has superior performance compared with some state-of-the-art methods.

关键词： Convolutional neural networks Face verification Without alignment Triplet loss

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水下机器人实时智能裂缝检测算法

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哈尔滨工程大学学报 2023年第5期44卷 774-782页

作者：邱昕捷韩凤磊赵望源哈尔滨工程大学船舶工程学院黑龙江哈尔滨150001

大坝水下部分的裂缝检测是大坝维护维修的主要技术难点,采用水下机器人检测维修是未来发展的趋势。为解决水下机器人实现裂缝检测过程中的水下图像增强、语义分割、裂纹识别以及裂纹面积计算等问题,本文基于该工程需求提出了改进金字塔... 详细信息

大坝水下部分的裂缝检测是大坝维护维修的主要技术难点,采用水下机器人检测维修是未来发展的趋势。为解决水下机器人实现裂缝检测过程中的水下图像增强、语义分割、裂纹识别以及裂纹面积计算等问题,本文基于该工程需求提出了改进金字塔场景解析网络算法,并对该算法神经网络结构的超参数进行了优化。针对水下作业需求提出了激光辅助测量的面积计算方法,并进行了真实环境实验验证。实验数据表明:与其他算法相比所提出的网络结构优化了损失值、平均交并比。研究表明:优化后的语义分割算法能满足水下机器人作业对不同形态裂缝检测的实时性、精准性要求。

关键词：人工智能裂缝检测计算机视觉图像处理金字塔场景解析网络语义分割神经网络深度学习水下

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Multi-label Classification of Remote Sensing image Based on deep learning on Embedded Device

Multi-label Classification of Remote Sensing Image Based on ...

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2021 International Conference on Computer, Blockchain and Financial Development, CBFD 2021

作者： Liu, Jingyuan Shi, Huajun China Electronics Technology Group Corporation N0.32 Research Institute Shanghai China

ISBN: (纸本)9781665412278

In recent years, the resolution of satellite remote sensing images has been continuously improved. People can obtain more useful data and information from remote sensing images. Remote sensing images are widely used in various fields such as resource surveys and weather forecasting. The classification of remote sensing images is a very critical link in remote sensing image processing. This article proposes a multi-label classification method for remote sensing images based on deep learning for embedded platforms. We use residual neural network as the basic model and use transfer learning to reduces the cost of model training, besides this, we use batch normalization, image augmentation, and self-defined loss evaluation index to improve the accuracy and generalization ability of the model. Finally, the model is deployed on the embedded platform NVIDIA Jetson TX2 through tensorflow lite, the model performs well on the embedded platform and has good real-time performance. © 2021 IEEE.

关键词： Remote sensing

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Lightweight Neural Network for real-time Crack Detection on Concrete Surface in Fog

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FRONTIERS IN MATERIALS 2021年 8卷

作者： Yao, Gang Sun, Yujia Yang, Yang Liao, Gang Chongqing Univ Sch Civil Engn Chongqing Peoples R China

Cracks are one of the most common factors that affect the quality of concrete surfaces, so it is necessary to detect concrete surface cracks. However, the current method of manual crack detection is labor-intensive and time-consuming. This study implements a novel lightweight neural network based on the YOLOv4 algorithm to detect cracks on a concrete surface in fog. Using the computer vision algorithm and the GhostNet Module concept for reference, the backbone network architecture of YOLOv4 is improved. The feature redundancy between networks is reduced and the entire network is compressed. The multi-scale fusion method is adopted to effectively detect cracks on concrete surfaces. In addition, the detection of concrete surface cracks is seriously affected by the frequent occurrence of fog. In view of a series of degradation phenomena in image acquisition in fog and the low accuracy of crack detection, the network model is integrated with the dark channel prior concept and the Inception module. The image crack features are extracted at multiple scales, and BReLU bilateral constraints are adopted to maintain local linearity. The improved model for crack detection in fog achieved an mAP of 96.50% with 132 M and 2.24 GMacs. The experimental results show that the detection performance of the proposed model has been improved in both subjective vision and objective evaluation metrics. This performs better in terms of detecting concrete surface cracks in fog.

关键词： crack detection deep learning concrete surface improved YOLOv4 ghostnet dark channel prior

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Three-dimensional reconstructing undersampled photoacoustic microscopy images using deep learning

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PHOTOACOUSTICS 2023年 29卷 100429页

作者： Seong, Daewoon Lee, Euimin Kim, Yoonseok Han, Sangyeob Lee, Jaeyul Jeon, Mansik Kim, Jeehyun Kyungpook Natl Univ Coll IT Engn Sch Elect & Elect Engn Daegu 41566 South Korea Kyungpook Natl Univ Inst Biomed Engn Sch Med Daegu 41566 South Korea Univ Calif Los Angeles Dept Bioengn Los Angeles CA 90095 USA

Spatial sampling density and data size are important determinants of the imaging speed of photoacoustic mi-croscopy (PAM). Therefore, undersampling methods that reduce the number of scanning points are typically adopted to enhance the imaging speed of PAM by increasing the scanning step size. Since undersampling methods sacrifice spatial sampling density, by considering the number of data points, data size, and the char-acteristics of PAM that provides three-dimensional (3D) volume data, in this study, we newly reported deep learning-based fully reconstructing the undersampled 3D PAM data. The results of quantitative analyses demonstrate that the proposed method exhibits robustness and outperforms interpolation-based reconstruction methods at various undersampling ratios, enhancing the PAM system performance with 80-times faster-imaging speed and 800-times lower data size. The proposed method is demonstrated to be the closest model that can be used under experimental conditions, effectively shortening the imaging time with significantly reduced data size for processing.

关键词： Photoacoustic microscopy deep learning Undersampled image Sparse sampling Three-dimensional reconstruction

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Distributed Intelligent Video Surveillance for Early Armed Robbery Detection Based on deep learning

Distributed Intelligent Video Surveillance for Early Armed R...

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Brazilian Symposium on Computer Graphics and image processing (SIBGRAPI)

作者： Sergio Fernandez-Testa Edwin Salcedo Centro de Investigación Desarrollo e Innovación en Ingeniería Mecatrónica Universidad Católica Boliviana “San Pablo” La Paz Bolivia

ISBN: (数字)9798350376036

ISBN: (纸本)9798350376043

Low employment rates in Latin America have contributed to a substantial rise in crime, prompting the emergence of new criminal tactics. For instance, “express robbery” has become a common crime committed by armed thieves, in which they drive motorcycles and assault people in public in a matter of seconds. Recent research has approached the problem by embedding weapon detectors in surveillance cameras; however, these systems are prone to false positives if no counterpart confirms the event. In light of this, we present a distributed IoT system that integrates a computer vision pipeline and object detection capabilities into multiple end-devices, constantly monitoring for the presence of firearms and sharp weapons. Once a weapon is detected, the end-device sends a series of frames to a cloud server that implements a 3DCNN to classify the scene as either a robbery or a normal situation, thus minimizing false positives. The deep learning process to train and deploy weapon detection models uses a custom dataset with 16,799 images of firearms and sharp weapons. The best-performing model, YOLOv5s, optimized using TensorRT, achieved a final mAP of 0.87 running at 4.43 FPS. Additionally, the 3DCNN demonstrated 0.88 accuracy in detecting abnormal situations. Extensive experiments validate that the proposed system significantly reduces false positives while autonomously monitoring multiple locations in real-time.

关键词： YOLO deep learning Training Computer vision Accuracy Tensors Weapons Cameras Video surveillance Servers

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LW-ISP: A Lightweight Model with ISP and deep learning

arXiv

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arXiv 2022年

作者： Chen, Hongyang Ma, Kaisheng Xi’an Jiaotong University Xi’an China Tsinghua University Beijing China

The deep learning (DL)-based methods of low-level tasks have many advantages over the traditional camera in terms of hardware prospects, error accumulation and imaging effects. Recently, the application of deep learning to replace the image signal processing (ISP) pipeline has appeared one after another;however, there is still a long way to go towards real landing. In this paper, we show the possibility of learning-based method to achieve real-time high-performance processing in the ISP pipeline. We propose LW-ISP, a novel architecture designed to implicitly learn the image mapping from RAW data to RGB image. Based on U-Net architecture, we propose the fine-grained attention module and a plug-and-play upsampling block suitable for low-level tasks. In particular, we design a heterogeneous distillation algorithm to distill the implicit features and reconstruction information of the clean image, so as to guide the learning of the student model. Our experiments demonstrate that LW-ISP has achieved a 0.38 dB improvement in PSNR compared to the previous best method, while the model parameters and calculation have been reduced by 23× and 81×. The inference efficiency has been accelerated by at least 15×. Without bells and whistles, LW-ISP has achieved quite competitive results in ISP subtasks including image denoising and enhancement. Copyright © 2022, The Authors. All rights reserved.

关键词： deep learning

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Efficient Autonomous Lumen Segmentation in Intravascular Optical Coherence Tomography images: Unveiling the Potential of Polynomial-Regression Convolutional Neural Network

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IRBM 2024年第1期45卷 100814-100814页

作者： Lau, Yu Shi Tan, Li Kuo Chee, Kok Han Chan, Chow Khuen Liew, Yih Miin Univ Malaya Fac Engn Dept Biomed Engn Kuala Lumpur Malaysia Univ Malaya Fac Med Dept Biomed Imaging Kuala Lumpur Malaysia Univ Malaya Fac Med Dept Med Kuala Lumpur Malaysia

Objectives: Intravascular optical coherence tomography (IVOCT) is a crucial micro-resolution imaging modality used to assess the internal structure of blood vessels. Lumen segmentation in IVOCT images is vital for measuring the location and the extent of vessel blockages and for guiding percutaneous coronary intervention. Obtaining such information in real-time is essential, necessitating the use of fast automated algorithms. In this paper, we proposed an innovative polynomial-regression convolutional neural network (CNN) for fast and automated IVOCT lumen *** and methods: The polynomial-regression CNN architecture was uniquely crafted to enable single pass extraction of lumen borders via IVOCT image regression, ensuring real-time processing efficiency without compromising accuracy. The architecture designed convolution for regression while omitting fully connected layers, leading to the spatial output of lumen representation as polynomial coefficients, thus enabling the formation of interconnected lumen points. The approach equipped the network to comprehend the intricate and continuous geometries and curvatures intrinsic to blood vessels in transverse and longitudinal dimensions. The network was trained on a dataset of 16,165 images and evaluated using 7,016 ***: The predicted segmentations exhibited a distance error of less than 2 pixels (26.40 mu m), Dice's coefficient of 0.982, Jaccard Index of 0.966, sensitivity of 0.980, specificity of 0.999, and a prediction time of 4 s (for a pullback containing 360 images). This technique demonstrated significantly improved performance in both accuracy and speed compared to published techniques. Conclusion: The strong segmentation performance, fast speed, and robustness to image variations highlight the practical clinical utility of the proposed polynomial-regression network. (c) 2023 AGBM. Published by Elsevier Masson SAS. All rights reserved.

关键词： Convolutional neural network deep learning Intravascular optical coherence tomography Lumen segmentation Polynomial regression

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Development of an autonomous fog computing platform using control-theoretic approach for robot-vision applications

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ROBOTICS AND AUTONOMOUS SYSTEMS 2022年 155卷

作者： Vinod, Dinsha SaiKrishna, P. S. Indian Inst Technol IIT Tirupati Dept Elect Engn Chindepalle Andhra Pradesh India

This paper presents the dynamic modelling and linear matrix inequality (LMI) based controller design of a distributed fog computing framework for real-time robot vision applications. A mobile robot vision system acquires the images from an application environment such as a warehouse, where articles are stacked in numerous racks. We characterise the mobile robot vision data (MRVD) using frames per second (FPS) and the image resolution. From the MRVD, object detection is performed by an open-source deep learning (DL) platform for detecting and localising various objects. However, with higher FPS together with high-resolution images, the processing time by the DL algorithm increases significantly. This necessitates the deployment of a distributed computing platform with several computing nodes. In this work, we deploy a distributed fog computing environment (DFCE) for the real-time object detection in an application environment. The processing time required to handle the MRVD is called the service time. However, for efficient auto-scaling performance, the mathematical model of the DFCE, taking into consideration the characteristics of the MRVD is necessary. In this context, we envisage the application of control theory to build the dynamic model of the DFCE. A Linear Parameter Varying (LPV) model is proposed for the DFCE with the service time as the output, the number of fog nodes as the input, and the characteristics of MRVD as the time-varying parameters. At first, an LPV model for the DFCE is derived using system identification, and the model is validated using the real-time test data. The LPV model is converted to a Polytopic LPV (PLPV) model for LMI based controller design. Finally, we develop and validate a Linear Matrix Inequality (LMI) based LPV controller to meet the service time constraints for a given application environment. For localisation and trajectory tracking with obstacle avoidance in the application environment, the mobile robot implements an Extend

关键词： Fog computing LPV modelling LMI based control deep learning for object detection Mobile robot SLAM

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