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Enhanced image classification using edge CNN (E-CNN)

VISUAL COMPUTER 2024年第1期40卷 319-332页

作者： Aldin, Shaima Safa Aldin, Noor Baha Aykac, Mahmut Al Nahrain Univ Continuing Educ Ctr Baghdad Iraq Hasan Kalyoncu Univ Dept Elect & Elect Engn Gaziantep Turkiye Gaziantep Univ Dept Elect & Elect Engn Gaziantep Turkiye

Recently, deep learning has become a hot topic in wide fields, especially in the computer vision that proved its efficiency in processing images. However, it tends to overfit or consumes a long learning time in many platforms. The causes behind these issues return to the huge number of learning parameters and lack or incorrect training samples. In this work, two levels of deep convolutional neural network (DCNN) are proposed for classifying the images. The first one is enhancing the training images with removing unnecessary details, and the second one is detecting the edges of the processed images for further reduction of learning time in the DCNN. The proposed work is inspired by the human eye's way in recognizing an object, where a piece of object can be helpful in the recognition and not necessarily the whole object or full colors. The goal is to speed up the learning process of CNN based on the preprocessed training samples that are precise and lighter to work well in real-time applications. The obtained results proved to be more significant for real-time classification as it reduced the learning process by (94%) in Animals10 dataset with a validation accuracy of (99.2%) in accordance with the classical DCNNs.

关键词： CNN Classification Training time Edge

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image processing based image to Cartoon Generation: Reducing complexity of large computation arising from deep learning

Image Processing based Image to Cartoon Generation: Reducing...

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Computational Intelligence and Sustainable Engineering Solutions (CISES), International Conference on

作者： Shruti Kumari Shrivastava Ruchi Gajjar Electronics and Communication Engineering Department Institute of Technology Nirma University Ahmedabad Gujarat India

This paper proposes an approach to convert real life images into cartoon images using image processing. The cartoon images have sharp edges, reduced colour quantity compared to the original image, and smooth colour regions. With the rapid advancement in artificial intelligence, recently deep learning methods have been developed for image to cartoon generation. Most of these methods perform extremely huge computations and require large datasets and are time consuming, unlike traditional image processing which involves direct manipulation on the input images. In this paper, we have developed an image processing based method for image to cartoon generation. Here, we perform parallel operations of enhancing the edges and quantizing the colour. The edges are extracted and dilated to highlight them in the output colour image. For colour quantization, the colours are assigned based on proposed formulation on separate colour channels. Later, these images are combined and the highlighted edges are added to generate the cartoon image. The generated images are compared with existing image processing approaches and deep learning based methods. From the experimental results, it is evident that the proposed approach generates high quality cartoon images which are visually appealing, have superior contrast and are able to preserve the contextual information at lower comnutational cost.

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image processing and deep learning Based Road Object Detection System for Safe Transportation

Image Processing and Deep Learning Based Road Object Detecti...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Rizvee Hassan Prito Md. Shafayat Hossain Md. Shajibul Islam Mehzabin Meem Md. Nawab Yousuf Ali Computer Science and Engineering Dept East West University Dhaka Bangladesh

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Road object detection, a pivotal task in computer vision and artificial intelligence, is dedicated to the identification and precise localization of a diverse array of elements on roadways, including vehicles, pedestrians, road signs, traffic lights, and potential obstacles. The essence of this task lies in its ability to provide real-time and precise object detection, ultimately serving as a crucial safeguard to prevent accidents and ensure the safety of drivers, passengers, and pedestrians. It also lays the foundation for advanced warning systems and aids in collision avoidance. Several popular models were implemented, encompassing YOLOv7, YOLOv7-Modified, YOLOv7-Tiny, YOLOv7-E6E, Faster R-CNN, and SSD. Among these, YOLOv7 achieved an impressive mean average precision (mAP) of 83.6%, with an inference speed of 15.1 ms, while YOLOv7E6E achieved the highest mAP of 86.2%, but with the cost of a slower inference speed of 30.3 ms. The modified version of YOLOv7 has produced 2.2% higher average precision accuracy (89.1%) than the main version of YOLOv7 due to its double RepVGG layers, skip connection and concatenation layers in the head architecture. To make this research accessible to a wider audience, a user-friendly web application is developed with an intuitive interface.

关键词： Location awareness Pedestrians Head Roads image processing Object detection real-time systems Safety Object recognition Vehicles

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Perceptible Lightweight Zero-Mean Normalized Cross-Correlation for Infrared Template Matching

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IEEE ACCESS 2024年 12卷 164777-164791页

作者： Lee, Seungeon Kim, Donyung Park, Inho Kim, Geonjong Kim, Sungho Yeungnam Univ Dept Elect Engn Gyongsan 38541 Gyeongsangbug D South Korea Hanwha Syst Seoul 04541 South Korea

Infrared template matching is an essential technology that enables reliable and accurate object detection, recognition, and tracking in complex environments. Perceptible Lightweight Zero-mean normalized cross-correlation (ZNCC) Template Matching (PLZ-TM) has been proposed as a tool for matching infrared images obtained from cameras with different fields of view. Aligning such images is challenging because of the involved differences in thermal distributions, focus discrepancies, background elements, and distortions. The first stage of PLZ-TM involves extracting feature maps from the search and template images using a deep learning network. This deep learning network is designed with a Convolutional Neural Network (CNN) architecture that omits pooling layers, thereby minimizing information loss during extraction. The subsequent stage involves matching the feature maps. The matching method utilizes a lightweight ZNCC (ZNCC) module that employs average pooling for training. The deep learning network is trained to optimize the distribution of the output heatmap and the probability at the correct location of the template image. PLZ-TM delivers excellent performance achieving a processing time of only 3.3 ms in matching a $640\times 480$ search image with a $192\times 144$ template image. Moreover, it attains a matching accuracy of 96% on a dataset obtained from infrared cameras with different fields of view.

关键词： Feature extraction deep learning image matching Accuracy Training Cameras real-time systems Brightness Object tracking Convolutional neural networks Infrared imaging Template matching zero-mean normalized cross correlation real-time infrared image matching convolutional neural network

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LMU-Net: lightweight U-shaped network for medical image segmentation

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2024年第1期62卷 61-70页

作者： Ma, Ting Wang, Ke Hu, Feng Southwest Petr Univ Chengdu Peoples R China Jiangsu Citron Biotech Co Ltd Nantong Peoples R China

deep learning technology has been employed for precise medical image segmentation in recent years. However, due to the limited available datasets and real-time processing requirement, the inherently complicated structure of deep learning models restricts their application in the field of medical image processing. In this work, we present a novel lightweight LMU-Net network with improved accuracy for medical image segmentation. The multilayer perceptron (MLP) and depth-wise separable convolutions are adopted in both encoder and decoder of the LMU-Net to reduce feature loss and the number of training parameters. In addition, a lightweight channel attention mechanism and convolution operation with a larger kernel are introduced in the proposed architecture to further improve the segmentation performance. Furthermore, we employ batch normalization (BN) and group normalization (GN) interchangeably in our module to minimize the estimation shift in the network. Finally, the proposed network is evaluated and compared to other architectures on publicly accessible ISIC and BUSI datasets by carrying out robust experiments with sufficient ablation considerations. The experimental results show that the proposed LMU-Net can achieve a better overall performance than existing techniques by adopting fewer parameters.

关键词： Medical image segmentation deep learning LMU-Net Lightweight networks

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deep learning and Machine learning for Malaria Detection: Overview, Challenges and Future Directions

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2024年第5期23卷 1745-1776页

作者： Jdey, Imen Hcini, Hazala Ltifi, Hela Sidi Bouzid Univ Kairouan Fac Sci & Technol Kairouan Tunisia Univ Sfax Natl Engn Sch Sfax ENIS ReGIM Lab Res Grp Intelligent Machines LR11ES48 Sfax Tunisia

Public health initiatives must be made using evidence-based decision-making to have the greatest impact. Machine learning algorithms are created to gather, store, process, and analyze data to provide knowledge and guide decisions. A crucial part of any surveillance system is image analysis. The communities of computer vision and machine learning have become curious about it as of late. This study uses a variety of machine learning, and image processing approaches to detect and forecast malarial illness. In our research, we discovered the potential of deep learning techniques as innovative tools with a broader applicability for malaria detection, which benefits physicians by assisting in the diagnosis of the condition. We investigate the common confinements of deep learning for computer frameworks and organizing, including the requirement for data preparation, preparation overhead, real-time execution, and explaining ability, and uncover future inquiries about bearings focusing on these constraints.

关键词： Malaria diagnosis machine learning deep learning convolutional neural network hybrid algorithms

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A lightweight deep learning method for real-time weld feature extraction under strong noise

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SIGNAL image AND VIDEO processing 2024年第11期18卷 8169-8184页

作者： Cheng, Jiaming Jin, Hui Southeast Univ Sch Civil Engn Jiangsu Key Lab Mech Anal Infrastructure & Adv Equ Nanjing Peoples R China

This paper proposes a lightweight deep learning (DL) framework for real-time accurate weld feature extraction from noisy images with light, smoke, or splash. Leveraging a two-dimensional human pose estimation paradigm, the framework follows a top-down architecture for accurate weld feature point localization. This study develops a semi-automatic annotation technique to dramatically reduce the annotation cost. Then, we design a lightweight yet faster You Only Look Once version 8 (YOLOv8) detector to rapidly detect the weld feature region in the presence of strong noise. To avoid reliance on high-resolution feature maps and achieve sub-pixel-level localization accuracy, a heatmap-free approach decomposes the feature point detection task into subtasks of horizontal and vertical coordinate classification. Comparison with mainstream DL-based weld recognition methods validates the superiority of the proposed method regarding real-time feature extraction accuracy and robustness.

关键词： Laser vision Pose estimation Weld feature extraction Coordinate classification YOLO

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deep learning-based framework for the observation of real-time melt pool and detection of anomaly in wire-arc additive manufacturing

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MATERIALS AND MANUFACTURING PROCESSES 2024年第6期39卷 761-777页

作者： Chandra, Mukesh Rajak, Sonu Vimal, K. E. K. Natl Inst Technol Patna Dept Mech Engn Patna India Natl Inst Technol Tiruchirappalli Dept Prod Engn Tiruchirappalli India Natl Inst Technol Patna Dept Mech Engn Patna 800005 India

Object detection has become a popular tool of deep learning in the era of digital manufacturing. In this study, the most powerful and efficient object detection algorithm, i.e., You Only Look Once (YOLO) algorithm, was used to detect anomalies in deposited beads of wire-arc additive manufacturing (WAAM) using melt pool images. This study used the latest version of YOLO algorithm to train and validate the custom image dataset of the melt pool obtained by conducting experiments using a robotic-controlled WAAM. The mean average precision (mAP) for the "Regular bead" class and the "Irregular bead" class reached 99% at an Intersection over Union (IoU) threshold of 0.5, for both training and validation. When the model was tested for new or unseen datasets by conducting four new experimental trials, the mAP value for the "Regular bead" class reached 98.47% and for the "Irregular bead" class reached 96.68% at an average processing time of 0.014 s/frame. The object detection algorithm YOLO has shown an excellent processing time of 15 ms per frame, which shows its potential for real-time application in the manufacturing industry.

关键词： WAAM deep learning object detection YOLOv8 real-time application

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real-time Self-Supervised Ultrasound image Enhancement Using Test-time Adaptation for Sophisticated Rotator Cuff Tear Diagnosis

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IEEE SIGNAL processing LETTERS 2025年 32卷 1635-1639页

作者： Lee, Haeyun Lee, Kyungsu Yoon, Jong Pil Kim, Jihun Kim, Jun-Young Korea Univ Technol & Educ Sch Comp Sci & Engn Cheonan 31253 South Korea Jeonbuk Natl Univ Dept Comp Sci & Artificial Intelligence Jeonju 54896 South Korea Kyungpook Natl Univ Sch Med Dept Orthoped Surg Daegu 41566 South Korea Kangnam Univ Div Elect & Semicond Engn Elect Engn Yongin 16979 South Korea Catholic Univ Daegu Sch Med Dept Orthoped Surg Daegu 42472 South Korea

Medical ultrasound imaging is a key diagnostic tool across various fields, with computer-aided diagnosis systems benefiting from advances in deep learning. However, its lower resolution and artifacts pose challenges, particularly for non-specialists. The simultaneous acquisition of degraded and high-quality images is infeasible, limiting supervised learning approaches. Additionally, self-supervised and zero-shot methods require extensive processing time, conflicting with the real-time demands of ultrasound imaging. Therefore, to address the aforementioned issues, we propose real-time ultrasound image enhancement via a self-supervised learning technique and a test-time adaptation for sophisticated rotational cuff tear diagnosis. The proposed approach learns from other domain image datasets and performs self-supervised learning on an ultrasound image during inference for enhancement. Our approach not only demonstrated superior ultrasound image enhancement performance compared to other state-of-the-art methods but also achieved an 18% improvement in the RCT segmentation performance.

关键词： Ultrasonic imaging image restoration Training real-time systems Self-supervised learning Biomedical imaging image enhancement Superresolution image resolution Medical diagnostic imaging Ultrasound image image enhancement test time adaptation rotator cuff tear

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The use of intelligent real-time image acquisition system using data mining technology in news acquisition and editing practice

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第2期24卷 639-656页

作者： Ma, Xiaowen Shandong Univ Arts Lib Jinan Shandong Peoples R China

Aiming to address the timely dissemination of news information, this work explores the clever utilization of data mining (DM) technology and deep learning (DL) algorithms to construct an intelligent real-time news image acquisition system to meet the urgency of news dissemination needs. First, this work introduces an intelligent real-time news image acquisition system and provides a detailed analysis of its principles and advantages. Throughout this process, the crucial role of DM technology in news image classification and automation is emphasized, especially in dealing with rapidly evolving news events. Next, the work establishes an intelligent real-time news image acquisition model based on DL algorithms, which integrates the essence of DM technology. Through this fusion, the research objective is to enhance the performance of the news image acquisition system to achieve higher real-time and accuracy, which is vital for the swift delivery of news information. Finally, this work investigates the application of the intelligent news image acquisition system in network communication to ensure its adaptability to various network communication scenarios while maintaining accuracy and real-time capabilities. The research results demonstrate that the application of DM technology in combination with DL algorithms can effectively meet the practical needs of the news industry, enhancing the automation of news image processing and enabling faster information delivery to the audience. Notably, the AlexNet model employed performs exceptionally well, achieving recognition rates of up to 99.6% after data augmentation or equalization processing, with an accuracy of 90.9% and a high specificity of 93.38%. This indicates outstanding overall classification accuracy and negative class accuracy, even when distinguishing between news and non-news scenarios. These results clearly underscore the connection between DM technology and news acquisition and editing practices, and emphasize it

关键词： deep learning data mining real-time image acquisition network security AlexNet model

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