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Indoor object recognition based on YOLOv5 with EIOU loss function 3

Indoor object recognition based on YOLOv5 with EIOU loss fun...

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3rd international conference on Advanced Algorithms and Signal image processing, AASIP 2023

作者： Qi, Weimin Chen, Hangong Ye, Yuting Yu, Guoshuai Engineering Training Center Jianghan University China Institute of Artificial Intelligence Jianghan University China

ISBN: (纸本)9781510668522

At present, large-scale object classification and recognition tasks inevitably encounter problems of training efficiency and model accuracy. The solution of this problem highly depends on the definition of loss function. A better loss function can get a more accurate model with fewer epoches, which undoubtedly helps to save computility and time cost and improve the security of the model. Nowadays, the task of indoor object recognition plays an important role in the fields of computer vision aided blind people, home robots and so on. However, the task of indoor object recognition is plagued by the above problems due to its huge category of recognition objects. Starting with the task of indoor object recognition, this paper innovatively uses EIOU loss function and YOLOv5 deep learning convolutional neural network in this field, which improves training efficiency and recognition accuracy. Some original bounding box regression loss functions of YOLO series (such as GIOU, DIOU, CIOU) have defects in that the prediction box coincides with the center point of the truth value box, and the horizontal vertical ratio is the same. The paper uses the EIOU loss function to solve this problem. This paper focuses on the network structure of YOLOv5, the defects of YOLOv5's native loss function, the calculation method and advantages of EIOU loss function, and compares the performance gap between EIOU and CIOU. Finally, the EIOU loss function is used to complete the indoor article identification task. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： computer vision

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High Dynamic Range image processing Techniques for Automation and Machine vision

High Dynamic Range Image Processing Techniques for Automatio...

引用

Automation, Robotics and computer Engineering (ICARCE), international conference on

作者： Chunhao Li Zhejiang University of Science and Technology Hangzhou China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

High Dynamic Range (HDR) imaging has become a significant technological advancement in visual data processing, allowing for the capture of a wider dynamic range of luminance levels in images. This paper explores various HDR processing techniques and their potential applications in automation and machine vision. By using methods such as multiple image fusion, image registration, and tone mapping, the paper demonstrates how HDR processing can enhance visual data in automated systems, improving accuracy in environments requiring complex lighting conditions. This work applies HDR algorithms to real-world scenarios, showcasing their potential in industrial automation and robotics, where accurate visual data plays a crucial role.

关键词： Visualization image registration Automation Accuracy Service robots Machine vision Lighting Real-time systems High dynamic range image fusion

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TAVM: A Novel Video Summarization Model Based on Text, Audio and Video Frames 3

TAVM: A Novel Video Summarization Model Based on Text, Audio...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Shambharkar, Prashant Giridhar Goel, Ruchi Delhi Technological University Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350342338

In today's digital world, the task of video summarization has gained immense importance within the realm of multimedia analysis. This relevance is largely driven by the exponential expansion in multimedia content consumption, encompassing audio, video, and images, which is readily available on-demand through various digital platforms. Automatic video summary is the process of creating a brief synopsis that summarizes the video by displaying its most useful and relevant elements, so consumers may rapidly comprehend the primary concept of a video without having to watch the entire material. Currently, the selection of the video segments to be included in the final summary is done in a variety of ways. The task is to analyze the video's multimedia data in search of relevant clues that will aid in decision-making. The proposed method TAVM (text, audio, and video mode) in this paper will provide the video summary using different multimedia elements of text, audio, and frames. The proposed TAVM method can be separated into three parts. The process begins with Video processing, where the BEiT vision transformer is employed to recognize objects within the chosen frame. Following that, Audio processing comes into play, which uses speech-to-text converters to transcribe the audio content. Finally, in the last step, the Summary Builder utilizes the GPT-3-based OpenAI API to generate a summary of the content. The experimental analysis on the benchmark dataset SumMe demonstrates the effectiveness of the proposed approach. © 2023 IEEE.

关键词： BEiT(BERT Pre-Training of image Transformers) vision transformer Key-frames Multimedia Analysis Video Summarization

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3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th Indian conference on computer vision, Graphics and image processing, ICVGIP 2018

3rd Workshop on Computer Vision Applications, WCVA 2018, hel...

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3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th Indian conference on computer vision, Graphics and image processing, ICVGIP 2018

ISBN: (纸本)9789811513862

The proceedings contain 10 papers. The special focus in this conference is on computer vision Applications. The topics include: AECNN: Autoencoder with Convolutional Neural Network for Hyperspectral image Classification;optic Disc Segmentation in Fundus images Using Anatomical Atlases with Nonrigid Registration;bird Species Classification Using Transfer Learning with Multistage Training;a Deep Learning Paradigm for Automated Face Attendance;Robust Detection of Iris Region Using an Adapted SSD Framework;dynamic image Networks for Human Fall Detection in 360-degree Videos;image Segmentation and Geometric Feature Based Approach for Fast Video Summarization of Surveillance Videos;supervised Hashing for Retrieval of Multimodal Biometric Data.

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Automatic Detection of Brain Tumor on MRI images Using a YOLO-Based Algorithm

Automatic Detection of Brain Tumor on MRI Images Using a YOL...

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Iranian Machine vision and image processing (MVIP)

作者： Masume Rahimi Mohammad Mostafavi Abazar Arabameri Faculty of Electrical and Computer Engineering University of Zanjan Zanjan Iran

This paper presents a study on the application of transfer learning and fine-tuning techniques to a deep learning model for the purpose of detecting three specific types of brain tumors from MRI images. The proposed approach utilizes the YOLO algorithm for automatic diagnosis. Specifically, the YOLOv4-tiny model, which is a smaller version of the YOLOv4 algorithm, was trained and evaluated due to its improved performance. The dataset utilized in this research is obtained from the figshare data repository, which comprises of labeled MRI images. The division of the dataset resulted in 80% for training, 10% for validation, and 10% for testing purposes. Additionally, a pre-processing technique was devised to enhance the features in the MRI images. The outcomes of the implementation demonstrate that the YOLOv4-tiny model obtained a mean average precision (mAP) of 0.8074 for the raw data and 0.8324 for the processed data.

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Trimap generation with background for natural image matting 3

Trimap generation with background for natural image matting

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3rd international conference on Optics and Machine vision, ICOMV 2024

作者： Fu, Qian Liang, Yihui Kun, Zou Feng, Fujian Xu, Xiang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science Zhongshan Institute University of Electronic Science and Technology of China Zhongshan China Guizou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China

ISBN: (纸本)9781510680319

image matting is a widely-used image processing technique that aims at accurately separating foreground from an image. However, this is a challenging and ill-posed problem that demands additional input, such as trimaps and background images, for providing prior knowledge. However, the manual annotation of trimaps require lots of labor, limiting the application of trimap-based methods. Some trimap-free methods explore alternatives with low labor requirements by utilizing captured background images, including background-based methods. However, the quality of alpha mattes predicted by trimap-free methods still fall short of trimap-based methods. To reduce the performance gap between background-based and trimap-based methodes, we present Trimap Generation from Background image (TG-BG) method which can generate trimaps from the input image and a captured background image. It provides an economical solution to facilitate the application of trimap-based methods, allowing for low-cost and high-quality alpha matte predictions. TP-BG leverages a ViT backbone for feature extraction and employs the image and Background Detail Fusion Stream (IBDFS) to capture multi-scale detail information. The introduction of foreground impact loss encourages the network to pay more attention to the foreground in the image. We validate the trimap prediction performance of TP-BG by comparing the alpha matte quality obtained by background-based methods and that obtained by trimap-based methods integrated with TP-BG. The experimental results demonstrate that TP-BG can generate high-quality trimap from a background image, and trimap-based methods integrated with TP-BG outperform the state-of-the-art background-based methods in terms of four alpha matte quality metrics. © 2024 SPIE.

关键词： Costs

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3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

3rd International Conference on Mobile Radio Communications ...

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3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

ISBN: (纸本)9789811979811

The proceedings contain 56 papers. The special focus in this conference is on Mobile Radio Communications and 5G Networks. The topics include: Predictive Analysis of Air Pollutants Using Machine Learning;Machine Learning Techniques Applied of Land Use—Land Cover (LULC) image Classification: Research Avenues Challenges with Issues;crime Analysis Using computer vision Approach with Machine Learning;natural Language processing Implementation for Sentiment Analysis on Tweets;VLSI Implementation of BCH Encoder with Triple DES Encryption for Baseband Transceiver;design and Implementation of image De-hazing Using Histogram Equalization;Improved Hybrid Unified Power Flow Controller Using Fractional Order PID Controlled Systems;Optimized Activation Function-Based SAR Ship Detection;framework for Implementation of Smart Driver Assistance System Using Augmented Reality;Power-Efficient Hardware Design of ECC Algorithm on High Performance FPGA;A Creative Domain of Blockchain Application: NFTs;sign Language Recognition Using Machine Learning;an IoT-Based Health Monitoring System for Stress Detection in Human Beings;ioT-Based Driving Pattern Analysis and Engine Sensor Damage Prediction Using Onboard Diagnostics;Solving the Element Detecting Problem in Graphs via Quantum Walk Search Algorithm (QWSA);critical Analysis of Secure Strategies Against Threats on Cloud Platform;pareto Optimal Solution for Fully Fuzzy Bi-criteria Multi-index Bulk Transportation Problem;automatic Candidature Selection by Artificial Natural Language processing;elimination and Restoring Deduplicated Storage for Multilevel Integrated Approach with Cost Estimation;vulnerability Assessment of Cryptocurrency Wallet and Exchange Websites;greedy Theory Using Improved Performance Prim’s Algorithm, Big Bang Speedup of the Bellman–Ford Algorithm;performance Analysis of High-Speed Optical Communication Systems Under the Impact of Four Wave Mixing.

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Spatial-Spectral Neural Network for High Resolution Multispectral image Classification

Spatial-Spectral Neural Network for High Resolution Multispe...

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Iranian Machine vision and image processing (MVIP)

作者： Roozbeh Tanha Hassan Ghassemian Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Classification of multispectral images in remote-sensing area having the capability to analyze and categorize diversified land cover. In this issue, extracting suitable spatial, spectral and even temporal features is one of the main challenges. Also, the existence of sufficient data required for the classification training process is another challenge, because in many cases it may not be available and we may not even have a reliable classification map. The use of neural networks for simultaneous feature extraction and classification is very popular and significant progress has been made in this field, but these networks usually have a high computational cost and require significant training data in the training process. In this work we propose a neural network for multispectral image classification purpose which requires few training samples and less calculation without using filterbanks for spatial feature extraction and it can improve classification accuracy by fusion of spatial and spectral features. The simulations indicate that the proposed method shows an acceptable performance.

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international Joint conference 2022 - 17th international Joint Symposium on Artificial Intelligence and Natural Language processing, iSAI-NLP 2022 and 3rd international conference on Artificial Intelligence and Internet of Things, AIoT 2022

International Joint Conference 2022 - 17th International Joi...

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2022 international Joint conference - 17th international Joint Symposium on Artificial Intelligence and Natural Language processing, iSAI-NLP 2022 and 3rd international conference on Artificial Intelligence and Internet of Things, AIoT 2022

ISBN: (纸本)9781665457279

The proceedings contain 41 papers. The topics discussed include: modeling of manufacturing processes using hidden semi-Markov model and RSSI data;image captioning for Thai cultures;forex price movement prediction using stacking machine learning models;enhancing response relevance and emotional consistency for dialogue response generation;convolutional time delay neural network for Khmer automatic speech recognition;association of serum uric acid and lipid parameters in patients at Lamphun Hospital, Thailand;factors affecting acceptance of dental appointment application among users in Bangkok and metropolitan area;sugarcane classification for on-site assessment using computer vision;portfolio optimization and rebalancing with transaction cost: a case study in the stock exchange of Thailand;and product and industrial classification code suggestion system for Thai language.

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Facial Expression Recognition with Quarantine Face Masks Using a Synthetic Dataset Generator

Facial Expression Recognition with Quarantine Face Masks Usi...

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3rd international conference on image processing and vision Engineering, IMPROVE 2023

作者： Çelik, Yücel Gören, Sezer Department of Computer Engineering Yeditepe University Istanbul Turkey

ISBN: (纸本)9789897586422

The usage of face masks has increased dramatically in recent years due to the pandemic. This made many systems that depended on a full facial analysis not as accurate on faces that are covered with a face mask, which may lead to errors in the system. In this paper, we propose a Convolutional Neural Network (CNN) model that was trained solely on face masks to be more accurate and on point, that could more easily determine facial expressions. Our CNN model was trained with a seven different expression category dataset that only had people with face masks. Although we could not find a suitable dataset with face masks, we opted to generate a synthetic one. The dataset generation was done using Python and the help of the OpenCV library. The process is, after finding the dimensions of the face, we Perspective Transform the face mask object to be able to overlay it on the face. After that, the CNN model was also generated using Python with a CNN model. Using this method we gathered favorable results on the test subjects with 70.1% accuracy on the validation batch where previous facial expression recognition systems mostly failed to even recognize the face since they were not trained to recognize faces with face masks. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： COVID-19

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