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10th International Conference on Mechatronics and Robotics Engineering (ICMRE)

作者： Ehrenfeuchter, Steffen Corlito, Roberto Marchthaler, Reiner Enzweiler, Markus Esslingen Univ Appl Sci Inst Intelligent Syst Esslingen Germany

ISBN: (纸本)9798350394283;9798350394276

This paper presents a real-time semantic segmentation framework for camera-based environment perception of objects and infrastructure elements in autonomous scale cars. It is specifically targeted towards student competitions such as the Carolo Cup or the Bosch Future Mobility Challenge. To reduce pixel-wise manual annotation efforts, our framework involves a mixture of both synthetic and real image data, carefully tuned towards the unique requirements of the given scenario. real images are acquired from a 1:10 scale vehicle equipped with a single monocular camera and are manually annotated. Synthetic image data with automatic pixel-wise annotation is obtained via a custom Unity-based simulation pipeline. We evaluate various mixed real-synthetic data strategies to train different state-of-the-art deep neural networks with a focus on both segmentation performance and real-time capability using an NVIDIA Jetson AGX Xavier platform as in-vehicle test bed. Our experimental results show a significant improvement in semantic segmentation performance of the mixed real-synthetic data approach at real-time speeds of approximately 60 FPS on the target platform.

关键词： Mobile Robotics Autonomous Systems Robot Intelligence and learning Signal and image processing

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Automated Brain Tumor Segmentation and Classification in MRI Using YOLO-Based deep learning

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IEEE ACCESS 2024年 12卷 16189-16207页

作者： Almufareh, Maram Fahaad Imran, Muhammad Khan, Abdullah Humayun, Mamoona Asim, Muhammad Jouf Univ Coll Comp & Informat Sci Dept Informat Syst Sakaka 72388 Saudi Arabia Univ Agr Inst Comp Sci & Informat Technol Fac Management & Comp Sci Peshawar 25130 Pakistan

Recent advancements in image processing and computer vision have brought significant transformations in healthcare technology, leading to significant improvements in diagnosis accuracy, cost-effectiveness, and time efficiency. Magnetic Resonance Imaging (MRI) is employed by the radiologist for its remarkable ability to detect even the most subtle brain abnormalities. This study considers a comprehensive analysis of the two prominent object identification frameworks, YOLOv5 and YOLOv7, leveraging state-of-the-art deep learning architectures to classify and detect brain cancers within MRI. The brain tumor dataset encompasses three distinct classes, including meningiomas, gliomas and pituitary tumors. To ensure precise segmentation of the tumor regions, the preprocessing phase incorporates advanced mask alignment techniques. This preprocessed dataset has been used to evaluate the performance of the deep learning models for brain tumor detection and classification. From the numerical results of YOLOv5, it was noticed that a recall score of 0.905 for box detection and 0.906 for mask segmentation, with a precision score of 0.94 and 0.936, respectively. At an IoU threshold of 0.5, both box detection and mask segmentation achieve a mAP of 0.947, whereas, at an IoU threshold of 0.5 to 0.95, they achieve mAPs of 0.666 and 0.657, respectively. In comparison, YOLOv7 exhibits strong performance with box detection accuracy of 0.936 and a mask segmentation accuracy of 0.935. The recall score are 0.904 for box detection and mask segmentation is 0.903. Notably, the mAP result at the IoU threshold of 0.5 are 0.94 for box detection and mask segmentation is 0.941. Over the broader IoU spectrum of 0.5 to 0.95, the mAP was 0.677 for box detection and 0.659 for mask segmentation. To underscore the novelty of the approach, the performance of the proposed framework is systematically compared with established methods such as RCNN, Faster RCNN, and Mask RCNN.

关键词： Brain tumor deep learning image processing MRI

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A Pre-processing Approach Using IBPDPNet for Single image Super Resolution

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2024年第1期49卷 35-48页

作者： Panda, Jagyanseni Meher, Sukadev SOA Univ Inst Tech Educ & Res Elect & Commun Engn Bhubaneswar 751030 Odisha India Natl Inst Technol Rourkela Elect & Commun Engn Sect 1 Rourkela 769008 Orissa India

The single image super-resolution (SISR) technique turns a low resolution (LR) image into a clear image. Many techniques, including edge-directed, dictionaries-based, and polynomial-based are offered for SISR. However certain blurring effects are formed when the LR image is converted to a higher resolution (HR) image. Recently, deep-learning methods based on convolutional neural networks (CNN) have emerged and are being used for super resolution. It is capable of restoring comprehensive information in HR images. However, these techniques are computationally costly and have significant memory needs, making them difficult to adapt to real-time settings. To overcome the aforementioned issue, iterative back projection-based dual path CNN (IBPDPNet) is utilized, with the LR image shallow and deep features extracted independently using the improved residual block and the recursive dilated convolution block. To reduce the blurring effects after upscaling, one lost detail information of the LR image in terms of back projecting error is added to the deep, and shallow features. To produce the essential features from the retrieved features channel-wise attention is applied followed by pixel shuffle for feature up-sampling. By implementing the aforesaid technique, the HR image retained with edge, texture, and geometric regularity is generated. The efficiency of the proposed strategy is better in terms of subjective and objective quality compared to the existing approach.

关键词： Blurring effects Error High frequency Low resolution Residual

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image and Video Captioning for Apparels Using deep learning

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IEEE ACCESS 2024年 12卷 113138-113150页

作者： Agarwal, Govind Jindal, Kritika Chowdhury, Abishi Singh, Vishal K. Pal, Amrit Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India Univ Essex Sch Comp Sci & Elect Engn Colchester Campus Colchester CO4 3SQ England

In the rapidly evolving world of apparel, writing clear and interesting product descriptions is crucial to attract customers. In light of the importance of automated descriptions for apparel, this work explores the field of image captioning for apparel photos and expands its use to include captioning videos to enable visually impaired people to access and understand dynamic apparel content. To address the issue of diversity in datasets, we curated a collection of images that were divided into 26 classifications. With the use of Convolutional Neural Network (CNN) architectures like ConvNeXtLarge and Long Short-Term Memory (LSTM) architectures, our suggested system can automatically provide accurate and captivating captions for both still photos and moving videos that feature clothing. The LSTM network smoothly blends the visual data extracted by the CNN component from clothing photos and videos to produce captions that are both semantically and linguistically accurate. In addition, a YOLO model is included for real-time object detection, which makes it possible for the model to precisely identify and track several articles of clothing at once. The suggested architecture is evaluated using the BLEU score performance metric;research on the selected dataset yielded a BLEU-1 score of 0.983 for the ConvNeXtLarge-based model.

关键词： Feature extraction Convolutional neural networks Long short term memory Clothing Visualization deep learning Computational modeling YOLO Apparel captioning BLEU score CNN ConvNeXtLarge deep learning LSTM

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Degradation-Aware Dynamic Fourier-Based Network for Spectral Compressive Imaging

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IEEE TRANSACTIONS ON MULtimeDIA 2024年 26卷 2838-2850页

作者： Xu, Ping Liu, Lei Zheng, Haifeng Yuan, Xin Xu, Chen Xue, Lingyun Hangzhou Dianzi Univ Sch Automat Hangzhou 310018 Peoples R China Fuzhou Univ Coll Phys & Informat Engn Fujian Key Lab Intelligent Proc & Wireless Transmi Fuzhou 350108 Peoples R China Westlake Univ Res Ctr Ind Future Hangzhou 310030 Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Peoples R China

We consider the problem of hyperspectral image (HSI) reconstruction, which aims to recover 3D hyperspectral data from 2D compressive HSI measurements acquired by a coded aperture snapshot spectral imaging (CASSI) system. Existing deep learning methods have achieved acceptable results in HSI reconstruction. However, these methods did not consider the imaging system degradation pattern. In this article, based on observing the initialized HSIs obtained by shifting and splitting the measurements, we propose a dynamic Fourier network based on degradation learning, called the degradation-aware dynamic Fourier-based network (DADF-Net). We estimate the degradation feature maps from the degraded hyperspectral images to realize the linear transformation and dynamic processing of the features. In particular, we use the Fourier transform to extract the HSI non-local features. Extensive experimental results show that the proposed model outperforms state-of-the-art algorithms on simulation and real-world HSI datasets.

关键词： image reconstruction Degradation Feature extraction Imaging Mathematical models Heuristic algorithms Convolution deep learning fourier transform hyperspectral images snapshot compressive imaging

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Towards an earthquake-resistant architectural design with the image classification method

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JOURNAL OF ASIAN ARCHITECTURE AND BUILDING ENGINEERING 2024年第1期23卷 157-170页

作者： Akan, Asli Er Bingol, Kaan Ormecioglu, Hilal Tugba Er, Arzu Ormecioglu, Tevfik Oguz Cankaya Univ Fac Architecture Dept Architecture Ankara Turkiye Middle East Tech Univ Informat Innovat Ctr Ankara Turkiye Akdeniz Univ Dept Architecture Fac Architecture Antalya Turkiye Akdeniz Univ Dept Construct Technol Tech Sci Vocat Sch Higher Educ Antalya Turkiye Akdeniz Univ Fac Engn Dept Civil Engn TR-07070 Antalya Turkiye

Architectural design is an interdisciplinary process which involves multiple stages that are interconnected. In this process, it is common for major decisions to be changed during the final stage, the analysis of the structural system. After making substantial corrections, the architect has to revisit the early stages, the preliminary project. This back-and-forth process can result in significant losses in time and cost. The proposed Irregularity Control Assistant (IC-Assistant) aims to provide architects with feedback on the conformity of structural system decisions to the irregularities defined in the Turkish Building Earthquake Code (TBEC-2018), using image processing methods at the early stages of the design process. The IC-Assistant was preliminarily created to evaluate the torsional irregularity of plan organization using deep learning methods. In this study, the results of the IC-Assistant were verified by structural analysis with the Prota-Structure program. The novelty of this study is the use of the image-classification method in earthquake-resistant architectural design. Up to this point, the method has been mainly used in facial recognition systems. This method minimizes time, human error, and cost losses and includes awareness of load bearing and earthquake resistance as inputs in the early stages of architectural design.

关键词： Earthquake resistant architectural design earthquake regulations machine learning deep learning artificial intelligence

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Fully Complex-Valued Gated Recurrent Neural Network for Ultrasound Imaging

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2024年第10期35卷 14918-14931页

作者： Lei, Zhenyu Gao, Shangce Hasegawa, Hideyuki Zhang, Zhiming Zhou, MengChu Sedraoui, Khaled Univ Toyama Fac Engn Toyama 9308555 Japan Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Peoples R China New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Newark NJ 07102 USA King Abdulaziz Univ Fac Engn Dept Elect & Comp Engn Jeddah 21589 Saudi Arabia King Abdulaziz Univ KA CARE Energy Res & Innovat Ctr Jeddah 21589 Saudi Arabia

Ultrasound imaging is widely used in medical diagnosis. It has the advantages of being performed in real time, cost-efficient, noninvasive, and nonionizing. The traditional delay-and-sum (DAS) beamformer has low resolution and contrast. Several adaptive beamformers (ABFs) have been proposed to improve them. Although they improve image quality, they incur high computation cost because of the dependence on data at the expense of real-time performance. deep-learning methods have been successful in many areas. They train an ultrasound imaging model that can be used to quickly handle ultrasound signals and construct images. real-valued radio-frequency signals are typically used to train a model, whereas complex-valued ultrasound signals with complex weights enable the fine-tuning of time delay for enhancing image quality. This work, for the first time, proposes a fully complex-valued gated recurrent neural network to train an ultrasound imaging model for improving ultrasound image quality. The model considers the time attributes of ultrasound signals and uses complete complex-number calculation. The model parameter and architecture are analyzed to select the best setup. The effectiveness of complex batch normalization is evaluated in training the model. The effect of analytic signals and complex weights is analyzed, and the results verify that analytic signals with complex weights enhance the model performance to reconstruct high-quality ultrasound images. The proposed model is finally compared with seven state-of-the-art methods. Experimental results reveal its great performance.

关键词： Analytic signal beamforming complex-valued neural network deep learning recurrent neural network (RNN) ultrasound imaging

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A position-adaptive noise-reduction method using a deep denoising filter bank for dedicated breast positron emission tomography images

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PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE 2024年第1期47卷 73-85页

作者： Tsukijima, Masahiro Teramoto, Atsushi Kojima, Akihiro Yamamuro, Osamu Tamaki, Tsuneo Fujita, Hiroshi East Nagoya Imaging Diag Ctr Imaging Diagnost Technol Dept 3-4-26 JiyugaokaChikusa Ku Nagoya Aichi Japan Fujita Hlth Univ Grad Sch Hlth Sci 1-98 DengakugakuboKutsukake Cho Toyoake Aichi Japan Nagoya PET Imaging Ctr 1-162 HokkeNakagawa Ku Nagoya Aichi Japan Gifu Univ Fac Engn 1-1 Yanagido Gifu Japan Meijo Univ Fac Engn Nagoya Japan

Dedicated breast positron emission tomography (db-PET) is more sensitive than whole-body positron emission tomography and is thus expected to detect early stage breast cancer and determine treatment efficacy. However, it is challenging to decrease the sensitivity of the chest wall side at the edge of the detector, resulting in a relative increase in noise and a decrease in detectability. Longer acquisition times and injection of larger amounts of tracer improve image quality but increase the burden on the patient. Therefore, this study aimed to improve image quality via reconstruction with shorter acquisition time data using deep learning, which has recently been widely used as a noise reduction technique. In our proposed method, a multi-adaptive denoising filter bank structure was introduced by training the training data separately for each detector area because the noise characteristics of db-PET images vary at different locations. Input and ideal images were reconstructed based on 1- and 7-min collection data, respectively, using list mode data. The deep learning model used residual learning with an encoder-decoder structure. The image quality of the proposed method was superior to that of existing noise reduction filters such as Gaussian filters and nonlocal mean filters. Furthermore, there was no significant difference between the maximum standardized uptake values before and after filtering using the proposed method. Taken together, the proposed method is useful as a noise reduction filter for db-PET images, as it can reduce the patient burden, scan time, and radiotracer amount in db-PET examinations.

关键词： Noise reduction deep learning Dedicated breast PET image processing

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Evaluation of Transfer learning for Visual Multiple Target Tracking 1

Evaluation of Transfer Learning for Visual Multiple Target T...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Jadhav, Anisha Anil California State University Department of Computer Science Monterey Bay Seaside United States

ISBN: (纸本)9798331518578

deep learning has revolutionized high-level image processing tasks, notably image classification and segmentation, by effectively handling multi-dimensional features in image space. This report investigates the application of transfer learning, utilizing features extracted from a pre-trained VGG deep neural network to visual multiple-target tracking (MTT). Traditional feature detectors like the Lucas-Kanade Tracker and Good Features to Track (GFTT) algorithm are computationally efficient but limited in dynamic, cluttered environments, such as those encountered by unmanned aerial systems (UAS). Replacing GFTT with deep feature detectors from VGG is proposed to assess tracking accuracy and improve computational efficiency. Our experiments reveal that, though promising, deep feature extraction results in lower frame rates and less smooth trajectories than traditional methods. This study highlights the potential and current limitations of integrating deep learning features into real-time visual tracking systems. Plans include optimizing the deep learning model for real-time processing, automating filter selection for feature extraction, and exploring additional pre-trained networks to improve tracking performance and efficiency. © 2025 IEEE.

关键词： image segmentation

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Biological Butterfly Characterization with Mobile System Using Convolutional Neural Network (CNN) Classify image

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第7期18卷 125-138页

作者： Saedan, Mohamad Aidiid Hafi Kassim, Murizah Abd Aziz, Azalina Farina School of Electrical Engineering Universiti Teknologi MARA Selangor Malaysia Selangor Malaysia

This study presents the development of a mobile identification system that detects biological butterfly characteristics through deep learning by capturing images. The challenge identified is that butterfly identification and recognition are difficult tasks because there are too many species, and it is hard to classify the types of butterfly species. Butterflies are also difficult to differentiate from each other, and limited studies are done using computer database referrals for butterflies’ characterization. This study aims to develop an automated computer program to easily identify the species of butterflies. deep learning in image processing is programmed, which can control the qualification, segmentation, and classification of images and automatically detect butterfly characterization. The design system consists of three stages: capture, feature extraction, and butterfly recognition. Then, multiple recognition clues such as shape, color, texture, and size are extracted and analyzed to analyze and recognize the butterfly. This approach is faster and less complex than the previous approach. The result successfully presents a convolutional neural network (CNN) to classify images after training and characterization. The graphics processing unit (GPU) that trains the image dataset presents 86% image accuracy in the allocated time. This research is significant in such a way that new butterfly species will be automatically collected and stored on the online server. The information could be treasured as a valuable butterfly database. © 2024 by the authors of this article.

关键词： Graphics processing unit

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