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Conference on Real-Time Processing of Image, Depth, and Video Information

作者： Bhattacharjee, Shovakar Shanmugam, Palanisamy Das, Sukhendu Indian Inst Technol Madras Ocean Engn Dept Chennai 600036 Tamil Nadu India Indian Inst Technol Madras Comp Sci & Engn Dept Chennai 600036 Tamil Nadu India

ISBN: (纸本)9781510673199;9781510673182

Satellite imagery-based ship detection is indispensable in maritime surveillance and monitoring the naval activities. Machine learning is an effective approach that enables the process to be automatic and more accurate as compared to many other approaches. Generally, optical and synthetic aperture radar satellite images are often employed for detecting/locating various marine activities using different methods. However, models trained on one set of images often yield large uncertainties when testing on other sets of images due to the complex scene characteristics. This study proposes a novel lightweight computationally efficient deep learning-based general ship detection model called the Multi-Attentive General Ship Detector (MAGSD) for detecting ships in both optical and SAR satellite images. The model is trained with the SAR Ship Dataset (SDD), which has ship instances from Gaofen-3 and Sentinel-1 SAR satellite images, and the MASATI dataset that contains ship instances from the Microsoft Bing Map. The proposed model focuses on the attention-guided convolutional neural network for extracting feature maps for detection, which bridges the gap between SAR and optical image characteristics constraints by focusing on different levels of convolutional features in the network. The model is built with a novel feature extractor that has fourteen convolutional layers with six max pool layers and six attention layers, connecting several convolutional points to focus on local features in different depth maps which serve as the backbone of the model. The comparative analysis showed the robustness of the proposed model over the state-of-the-art baseline model YOLOv5s, with a precision of 8.2% and a recall of 9.63%. These results indicate that the proposed model holds the potential to serve as an efficient tool for ship detection in any satellite images and contributes to the enhanced coastal surveillance and bolsters global naval security.

关键词： Synthetic aperture radar Ocean optics Satellite imaging Satellites Object detection computer vision technology Deep learning Machine learning

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MizSIM: A Headless Open-Source Simulation Framework for Training and Evaluating Artificial Intelligence 2

MizSIM: A Headless Open-Source Simulation Framework for Trai...

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Conference on Synthetic Data for Artificial Intelligence and Machine Learning - Tools, Techniques, and Applications II

作者： Soloviov, Andrii Anderson, Derek T. Buck, Andrew R. Alvey, Brendan Univ Missouri Elect Engn & Comp Sci Dept Columbia MO 65211 USA

ISBN: (纸本)9781510673892;9781510673885

Advancements in deep learning have revolutionized the artificial intelligence (AI) landscape. However, despite considerable performance enhancements, their reliance on data and the intrinsic opacity of these models remains a challenge, hindering our ability to understand the reasons behind their failures. This paper introduces a headless open-source framework, coined MizSIM, built on the Unreal Engine (UE) to generate high volume and variety synthetic datasets for AI training and evaluation. Through the manipulation of agent and environment parameters, MizSIM can provide detailed performance analysis and failure diagnosis. Leveraging UE's opensource distribution, cost-effective assets, and high-quality graphics, along with tools like AirSim and the Robotic Operating System (ROS), MizSIM ensures user-friendly design and seamless data extraction. In this article, we demonstrate two MizSIM workflows: one for a single-life computer vision task and the other to evaluate an object detector across hundreds of simulated lives. The overarching aim is to establish a closed-loop environment to enhance AI effectiveness and transparency.

关键词： Artificial intelligence Education and training Object detection Sensors computer simulations computer vision technology Robotics Visualization

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Staged multi-target tracking and matching algorithm combined with depth estimation information

Staged multi-target tracking and matching algorithm combined...

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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Jia, Yunhua Zhou, Chengjiang Yunnan Normal University School of Information Kunming650500 China Pattern Recognition and Artificial Intelligence Laboratory Kunming650500 China

ISBN: (纸本)9781510680425

As the field of artificial intelligence progresses, multi-target tracking, one of the advanced visual tasks of computer vision, has developed *** present, as multi-target tracking technology gradually matures, related scientific research results are slowly appearing in our daily lives, bringing more powerful help to our ***, the precision of multi-target tracking faces significant constraints, mainly attributed to factors like occlusion and interference from the *** to reduce the constraints of occlusion and other factors on the accuracy of multi-target *** this paper, we propose a staged multi-target tracking and matching algorithm that combines depth estimation *** using the depth estimation information of targets in the scene, combined with the proposed staged matching algorithm improves the precision of the algorithm for multi-target tracking. © 2024 SPIE.

关键词： Detection and tracking algorithms Ablation Algorithm development Data modeling computer vision technology Sensors Video

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On-line measurement of needle-like crystal sizes based on computer vision methodology 5

On-line measurement of needle-like crystal sizes based on co...

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5th International Conference on computer vision and Data Mining, ICCVDM 2024

作者： Min, Yufan Deng, Le Che, Yuxuan Zhang, Jingxue Cui, Shiqi Huo, Yan School of Information Engineering Shenyang University Shenyang110044 China

ISBN: (纸本)9781510682863

The crystal size is an important parameter for monitoring and control in industrial crystallization processes. In this paper, we put forward an optimized measuring technology for size measurement by using computer vision methodologies. The focus is to overcome the uneven background with filtering and deblurring methods, image segmentation, and size measurement. The crystals can be segmented from the crystallization images by the improved combination method. Then the particle sizes are obtained by fitting the crystal shapes for analysing the particle size distribution. The experimental results present the proposed model has high effectiveness based on real-time crystallization images. © 2024 SPIE.

关键词： Crystals Image processing Image segmentation Crystallization computer vision technology

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Use neural Networks to Recognize Students' Handwritten Letters and Incorrect Symbols

Use neural Networks to Recognize Students' Handwritten Lette...

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2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Zhu, Jia Jun Yang, Zichuan Hong, Binjie Song, Jiacheng Wang, Jiwei Chen, Tianhao Yang, Shuilan Lan, Zixun Ma, Fei Department of Information and Computing Science Xi'an Jiaotong-Liverpool University Suzhou China School of Engineering Suzhou Centennial College Suzhou China Department of Applied Mathematics Xi'an Jiaotong-Liverpool University Suzhou China GROW Inc. Suzhou China EasyStack Inc. Nanjing China TAL Education Group Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Correcting students' multiple-choice answers is a repetitive and mechanical task that can be considered an image multi-classification task. Assuming possible options are 'abcd' and the correct option is one of the four, some students may write incorrect symbols or options that do not exist. In this paper, five classifications were set up - four for possible correct options and one for other incorrect writing. This approach takes into account the possibility of non-standard writing options. © 2025 SPIE.

关键词： Education and training Machine learning Data modeling Neural networks Image classification Visual process modeling computer vision technology Deep learning Performance modeling Transformers

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Small target detection algorithm based on improved YOLOv5 4

Small target detection algorithm based on improved YOLOv5

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4th International Conference on Internet of Things and Smart City, IoTSC 2024

作者： Li, Yushan Zhu, Yuanjing Hu, Peng Wang, Honglin Ding, Hongwei College of Information Science and Engineering Yunnan University Yunnan Kunming650000 China College of Science and Technology Dianchi College of Yunnan University Yunnan Kunming650000 China Youbei Technology Co. Ltd. Yunnan Kunming650000 China

ISBN: (纸本)9781510681767

At present, the Internet and smart cities are developing rapidly, and all social services. Small target detection is widely used in these fields. This paper focuses on small target detection based on YOLOv5. The feature enhancement module is proposed to solve the problem of incomplete extraction of small target features by the model. An attention mechanism is added to the model, so that the detection model pays more attention to the region in the image that contains the target to be detected, increases the weight of the feature information in this part, enriches the feature space, and further improves the performance of small target detection. Finally, by combining the use of network pruning and knowledge distillation, the model is compressed to compress the model size and improve the detection speed under the premise of ensuring that the model detection accuracy is not affected. The experimental results show that our optimisation effectively improves small target detection in terms of both detection accuracy and detection speed, with FPS improved by 31.84 and AP improved by 1.7%. © 2024 SPIE.

关键词： Object detection Target detection Small targets Performance modeling Detection and tracking algorithms computer vision technology Feature extraction Mathematical optimization Pattern recognition

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Enhanced Feline Facial Recognition: Advancing Cat Face Detection with YOLOv8 and TensorRT 4

Enhanced Feline Facial Recognition: Advancing Cat Face Detec...

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4th International Conference on computer vision and Pattern Analysis, ICCPA 2024

作者： Wang, Teng University College Durham University DurhamDH1 3LE United Kingdom

ISBN: (纸本)9781510682528

In the field of computer vision, the ability to accurately detect and recognise animal features in various environments is an area of growing interest and application. This study presents an advanced cat face detection system utilising the You Only Look Once Version 8 (YOLOv8) architecture, enhanced with TensorRT optimisation for real-time processing. The approach involves a comprehensive data augmentation process to improve detection accuracy across diverse cat breeds and environmental conditions. Performance evaluation is based on quantifiable metrics;the optimised model achieves a notable reduction in inference time from 50.1ms to 0.9ms and a decrease in GPU power usage from 77 watts to 63 watts, without compromising accuracy. The accelerated processing speed and reduced power requirements make the system highly suitable for real-time applications, such as pet monitoring systems or behaviour analysis tools, where rapid and accurate detection is paramount. The research highlights the potential of deep learning algorithms in precise animal feature recognition and contributes to the field of computer vision by addressing challenges in small, diverse object detection. © 2024 SPIE.

关键词： Object detection Data modeling Performance modeling Education and training Facial recognition systems Animals computer vision technology Deep learning

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An improved Yolov8-based pest detection model for detecting elongate larvae 4

An improved Yolov8-based pest detection model for detecting ...

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4th International Conference on computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Yi, Zhoujian Huo, Zijie Lai, Yuqi Yin, Zhjian Li, Jun Yang, Zhen School of Information and Mechanical and Electrical Engineering Jiangxi Science and Technology Normal University Nanchang330000 China School of Education Jiangxi Science and Technology Normal University Nanchang330000 China CECEP Lattice Technology Co. Ltd Nanchang330000 China

ISBN: (纸本)9781510683242

Agriculture, an integral part of human existence, plays a central role in the economic and strategic sphere of every country. Crop productivity is of concern to all, as pests and diseases can significantly reduce yields or even lead to the death of crops. Therefore, the detection of such threats is of utmost importance. With the advances in deep learning, the application of object detection algorithms to identify and monitor these problems has gained momentum. The present work uses the improved YOLOv8 algorithm as a foundation and introduces extensions that show improved performance on the widely recognized IP102 dataset intended for public insect images. © 2024 SPIE.

关键词： Object detection Agriculture Diseases and disorders Detection and tracking algorithms Convolution Data modeling Target detection computer vision technology Education and training Feature extraction

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Literature omics: a new approach to systematic review about pose estimation 9

Literature omics: a new approach to systematic review about ...

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9th International Symposium on Advances in Electrical, Electronics, and computer Engineering, ISAEECE 2024

作者： Ma, Mingtao School of Computer Shandong Technology and Business University Shandong Yantai264005 China

ISBN: (纸本)9781510683303

Background Pose estimation has been developed and is frequently used in motion capture and other applications. But it is still unclear "what exactly drives the development of pose estimation". And no summary of the pose estimate milestones has been provided. Objective The purpose of this paper is to examine the evolution of the field of pose estimation, identify significant developments (in terms of time, person, publications, institutions, nations, and their internal connections), and identify significant turning points and milestones. In addition, this paper aims to investigate pose estimation research. Methods This paper conducts analysis of related literatures using the method of literatureomics based on 4873 literatures related to pose estimation that were retrieved from the Web of Science Core Collection. Perform co-authorship and keyword co-occurrence analysis and visualization using VOSviewer, and identify emerging keywords and reference literature using CiteSpace. Additionally, analyze milestone events over time by combining them with thematic modeling discussions. Conclusions This study conducted a bibliometric analysis of over 20 years of research on pose estimation, identifying the countries, institutions, authors, journals, and publications involved in this field. Pose estimation has grown in popularity thanks to Deep Learning and COCO datasets, and it will be extensively employed in the surgical sector. © 2024 SPIE.

关键词： Pose estimation Analytical research Deep learning Modeling Algorithm development Lithium Object detection computer vision technology Statistical analysis Visualization

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Special Section Guest Editorial: Biologically Inspired computer vision and Image Processing

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JOURNAL OF ELECTRONIC IMAGING 2022年第4期31卷

作者： Yu, Keping Wang, Wei Tariqc, Muhammad Waseda Univ Global Informat & Telecommun Inst Tokyo Japan Sun Yat Sen Univ Guangzhou Peoples R China Natl Univ Comp & Emerging Sci Islamabad Pakistan

Guest Editors Keping Yu, Wei Wang, Moayad Aloqaily, Muhammad Tariq introduce the Special Section on Biologically Inspired computer vision and Image Processing.

关键词： Iris recognition Machine vision computer vision technology Image processing Image segmentation Convolutional neural networks Eye models Feature extraction Visual process modeling Optimization (mathematics)

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