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Relevant bibliographies by topics / Custom MobileNetV3

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Academic literature on the topic 'Custom MobileNetV3'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Custom MobileNetV3"

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Mitro, Satu, Asif Shakil Ahamed, and Arman Mohammad Nakib. "Comparative Study of Lightweight CNN Architectures for Maize Leaf Disease Detection." European Journal of Applied Science, Engineering and Technology 3, no. 2 (2025): 4–13. https://doi.org/10.59324/ejaset.2025.3(2).01.

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The study evaluates different compact Convolutional Neural Networks (CNNs) used to detect maize leaf diseases because they serve vital functions in precision agriculture. Testing involved evaluating the performance of five various models including VGG19, ResNet50, MobileNetV3, Custom MobileNetV3 and Custom InceptionV3 for the detection of four maize leaf disease types namely Blight, Common Rust, Gray Leaf Spot and Healthy. The analysis demonstrates that Custom MobileNetV3 surpasses all competing models through its 97.63% accuracy and 96.68% precision rating as well as 97.96% recall value. The

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Mitro, Satu, Asif Shakil Ahamed, and Arman Mohammad Nakib. "Comparative Study of Lightweight CNN Architectures for Maize Leaf Disease Detection." European Journal of Applied Science, Engineering and Technology 3, no. 2 (2025): 4–13. https://doi.org/10.59324/ejaset.2025.3(2).01.

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The study evaluates different compact Convolutional Neural Networks (CNNs) used to detect maize leaf diseases because they serve vital functions in precision agriculture. Testing involved evaluating the performance of five various models including VGG19, ResNet50, MobileNetV3, Custom MobileNetV3 and Custom InceptionV3 for the detection of four maize leaf disease types namely Blight, Common Rust, Gray Leaf Spot and Healthy. The analysis demonstrates that Custom MobileNetV3 surpasses all competing models through its 97.63% accuracy and 96.68% precision rating as well as 97.96% recall value. The

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Zhou, Chenggang, Jie Pi, Xiao Chen, Daoying Wang, and Jun Liu. "Identification and Analysis of Pork Freshness Quality Based on Improved MobileNetV3." Applied Engineering in Agriculture 41, no. 1 (2025): 57–66. https://doi.org/10.13031/aea.16131.

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HighlightsModel can perform pork freshness quality testing under complex lighting.MobileNetV3_E outperforms other state-of-the-art models.Model achieve an optimal balance between performance and volume.Model supports deployment on edge devices.Abstract. The accurate appraisal of freshness is influenced by the color of the meat, which is a critical indicator of pork’s freshness. However, lighting changes can also influence consumers’ perceptions of meat color. To address this issue, this study recommends a pork freshness detection method based on the lightweight MobileNetV3 model that can effec

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Bandan., Sheikh Sadi, MD Samiul Islam Sabbir., Md Sharuf Hossain., and Khadiza Tul Kobra. "Enhancing Fashion Choices: AI-Powered Style Analysis and Recommendations." International Journal of Research and Innovation in Applied Science IX, no. VIII (2024): 478–90. http://dx.doi.org/10.51584/ijrias.2024.908042.

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Fashion has always been an essential feature of our daily routine. It plays an important role in everyone’s life. The online fashion market continues to grow, and an algorithm capable of identifying clothing can help companies in the apparel industry understand the profile of potential buyers and focus sales on specific niches. Artificial intelligence capable of understanding, recommending and labeling human clothing is essential, and can be used to improve sales or better understand users. In this paper, we used our own generated dataset, where the total number of data was 1000. The dataset c

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Yi, Lingjie, Xianzhong Xie, Yi Wan, Bo Jiang, and Junfan Chen. "Custom Network Quantization Method for Lightweight CNN Acceleration on FPGAs." International Journal of Distributed Sensor Networks 2024 (April 2, 2024): 1–11. http://dx.doi.org/10.1155/2024/8018810.

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The low-bit quantization can effectively reduce the deep neural network storage as well as the computation costs. Existing quantization methods have yielded unsatisfactory results when being applied to lightweight networks. Additionally, following network quantization, the differences in data types between the operators can cause issues when deploying networks on Field Programmable Gate Arrays (FPGAs). Moreover, some operators cannot be accelerated heterogeneously on FPGAs, resulting in frequent switching between the Advanced RISC Machine (ARM) and FPGA environments for computation tasks. To a

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Tri Vicika, Vikha, Jamaludin Indra, Sutan Faisal, and Hanny Hikmayanti. "Improvement of FPS and Efficiency of Parameters Mask R-CNN with MobileNetV3 Small for Cardboard Detection." Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 16, no. 1 (2025): 26–36. https://doi.org/10.31849/q13aq917.

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Inventory management in warehouses often experiences discrepancies in recording the number of cardboard boxes due to errors during the manual recording process. To overcome this problem, a cardboard detection method was developed using the Default Mask R-CNN model and a modified model using MobileNetV3 Small. The training data was obtained from a collection of cardboard photos which then went through an annotation stage. In the cReonfiguration stage, various anchor scales were applied to determine the bounding box parameters, while the training process used Stochastic Gradient Descent (SGD). T

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Altun, Murat, Hüseyin Gürüler, Osman Özkaraca, Faheem Khan, Jawad Khan, and Youngmoon Lee. "Monkeypox Detection Using CNN with Transfer Learning." Sensors 23, no. 4 (2023): 1783. http://dx.doi.org/10.3390/s23041783.

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Monkeypox disease is caused by a virus that causes lesions on the skin and has been observed on the African continent in the past years. The fatal consequences caused by virus infections after the COVID pandemic have caused fear and panic among the public. As a result of COVID reaching the pandemic dimension, the development and implementation of rapid detection methods have become important. In this context, our study aims to detect monkeypox disease in case of a possible pandemic through skin lesions with deep-learning methods in a fast and safe way. Deep-learning methods were supported with

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Yuldashev, Yusufbek, Mukhriddin Mukhiddinov, Akmalbek Bobomirzaevich Abdusalomov, Rashid Nasimov, and Jinsoo Cho. "Parking Lot Occupancy Detection with Improved MobileNetV3." Sensors 23, no. 17 (2023): 7642. http://dx.doi.org/10.3390/s23177642.

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In recent years, parking lot management systems have garnered significant research attention, particularly concerning the application of deep learning techniques. Numerous approaches have emerged for tackling parking lot occupancy challenges using deep learning models. This study contributes to the field by addressing a critical aspect of parking lot management systems: accurate vehicle occupancy determination in specific parking spaces. We propose an advanced solution by harnessing an optimized MobileNetV3 model with custom architectural enhancements, trained on the CNRPark-EXT and PKLOT data

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Jouini, Oumayma, Mohamed Ould-Elhassen Aoueileyine, Kaouthar Sethom, and Anis Yazidi. "Wheat Leaf Disease Detection: A Lightweight Approach with Shallow CNN Based Feature Refinement." AgriEngineering 6, no. 3 (2024): 2001–22. http://dx.doi.org/10.3390/agriengineering6030117.

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Improving agricultural productivity is essential due to rapid population growth, making early detection of crop diseases crucial. Although deep learning shows promise in smart agriculture, practical applications for identifying wheat diseases in complex backgrounds are limited. In this paper, we propose CropNet, a hybrid method that utilizes Red, Green, and Blue (RGB) imaging and a transfer learning approach combined with shallow convolutional neural networks (CNN) for further feature refinement. To develop our customized model, we conducted an extensive search for the optimal deep learning ar

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Thor, Wen Zheng, and Vasuky Mohanan. "Cat Body Language Recognition Using Computer Vision in an Android Application." Journal of International Conference Proceedings 8, no. 1 (2025): 347–64. https://doi.org/10.32535/jicp.v8i1.3999.

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Understanding cat behaviour is essential for fostering healthy human-cat relationships, but its inherent complexity frequently leads to misunderstandings. This study introduces Emeowtions, an innovative Android application employing artificial intelligence (AI) to decipher cat emotions and body language in real-time. Addressing market gaps for comprehensive tools, Emeowtions integrates the YOLOv8n object detection model with a custom-trained multi-label classification model for cat emotion and body language analysis. The custom model was developed based on the CRoss Industry Standard for Data

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Book chapters on the topic "Custom MobileNetV3"

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Akram, Omar, Abdelrahman Mohamed, Hager Magdy, Mariam M. Abdellatif, and Sara Abdelghafar. "Comparative Analysis of Custom CNN Architecture and MobileNet for Deepfake Image Detection." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81308-5_6.

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Vasudevan, Swetha, and Pon Harshavardhanan. "Ensemble Learning for Breast Cancer Prediction Using FNA Histopathology Images." In Artificial Intelligence Transformations for Healthcare Applications. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-7462-7.ch009.

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In this chapter, prediction of breast cancer has been carried out from fine needle aspiration (FNA) histopathology images using TensorFlow. The prediction accuracy has been improved by ensembling of neural networks with a custom NN and 2 pre-trained models MobileNet and VGG16. The FNA images are augmented and combined by concatenation, average and weighted average. Performance metrics like accuracy, loss, classification reports, and confusion matrices have been used. When these highly accurate and efficient models are employed for medical purposes, it serves as an invaluable assistance for ear

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Conference papers on the topic "Custom MobileNetV3"

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Santana, Vitor José Ferreira dos Santos de, Humberto José da Silva Júnior, Frank César Lopes Véras, and Daniel Louçana da Costa Araújo. "Classificando extratos vegetativos de uma área do Bioma Caatinga com imagens de VANTs." In Encontro Unificado de Computação do Piauí. Sociedade Brasileira de Computação, 2023. http://dx.doi.org/10.5753/enucompi.2023.26613.

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Este artigo apresenta um VANT de baixo custo que foi montado e utilizado na classificação binária do extrato herbáceo de uma área do bioma Caatinga, com o objetivo de possibilitar a captura e classificação de imagens de forma mais acessível. O alto custo dessas aeronaves tem sido uma barreira para pesquisas e desenvolvimento agrícola em regiões desfavorecidas no Brasil. Neste estudo, a captura e o processamento das imagens exerceram papel importante na classificação binária, submetendo-as à Rede Neural MobileNetV2. Os resultados alcançaram uma acurácia de 93.7% e um índice kappa de 0.79, evide

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Guzzo, Luiz Antonio Roque, and Kelly Assis de Souza Gazolli. "Utilizando a Arquitetura UNet++ na Estimativa de Profundidade Monocular." In Seminário Integrado de Software e Hardware. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/semish.2023.229972.

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Com o surgimento das redes convolucionais, muitas abordagens foram propostas visando melhorar os resultados na estimativa de profundidade, mas desconsiderando os custos computacionais. Neste trabalho, apresentamos uma abordagem que utiliza a arquitetura UNet++, empregando uma rede MobileNetV2 como codificador, gerando uma estrutura mais leve, com um número menor de parâmetros. Os experimentos realizados na base NYU Depth V2 mostraram que é possível alcançar melhores resultados quando comparado a trabalhos anteriores, mantendo, no entanto, uma estrutura mais simples.

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Sá, Luís Clício Carvalho, Vinícius de Sousa Carvalho, José Wanderlei Francisco de Sousa Rocha, and Romuere Rodrigues Veloso e Silva. "Detecção de Rachaduras em Concreto em Imagens com o uso de Redes Neurais Convolucionais." In Encontro Unificado de Computação do Piauí. Sociedade Brasileira de Computação, 2023. http://dx.doi.org/10.5753/enucompi.2023.26616.

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Rachaduras em infraestruturas de concreto podem causar danos estruturais graves e representam uma ameaça para a segurança pública, especialmente nas áreas próximas a essas estruturas. A inspeção dessas estruturas, em sua maioria, é difícil e dispendiosa, além de estar sujeita à subjetividade da avaliação do inspetor. Uma possível solução seria utilizar drones que empreguem técnicas de visão computacional, visando inspeções mais eficientes e seguras. No entanto, é necessário levar em consideração o custo de implementação, influenciado grandemente pelos recursos disponíveis nos drones. Desta for

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Zhan, Tian, and Austin Amakye Ansah. "Enhancing Amateur Photography: A Deep Learning Mobile Application for Real-Time Aesthetic feedback." In 12th International Conference on Computational Science and Engineering. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141602.

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Capturing aesthetically pleasing photographs can be challenging for amateur photographers due to the complexity of factors such as lighting, composition, and contrast. To address this issue, we propose a mobile application powered by deep learning models and regression analysis. This application analyzes real-time image frames using a pre-trained MobileNet backbone and a custom classification layer [8]. By leveraging the Aesthetics and Attributes database, the app calculates an aesthetic score for each photograph, providing instant feedback to users. Challenges encountered during development,

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Adham Ibrahim, Alaa, and Polla Fattah. "Multi-Factor Classification Using Deep Learning for X-Ray Image Classification." In The 3rd International Conference on Engineering and Innovative Technology. Salahaddin University-Erbil, 2025. https://doi.org/10.31972/iceit2024.047.

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Medical imaging is a pivotal tool in modern diagnostics, offering detailed visualization of internal body structures. Skeletal radiographs (X-rays) are essential for assessing bone conditions and anomalies. This research aims to enhance diagnostic precision and efficiency by leveraging deep learning models to predict patients' age and gender from chest radiographs. We aim to evaluate the performance of pre-trained models (VGG-16, MobileNet, EfficientNetB0) against a custom ResNet-50 model designed for skeletal radiograph analysis. A dataset of chest X-ray images was collected and preprocessed

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Fairuzi, Muhammad Reza, and Fitri Yuli Zulkifli. "Performance Analysis of YOLOv4 and SSD Mobilenet V2 for Foreign Object Debris (FOD) Detection at Airport Runway Using Custom Dataset." In 2021 17th International Conference on Quality in Research (QIR): International Symposium on Electrical and Computer Engineering. IEEE, 2021. http://dx.doi.org/10.1109/qir54354.2021.9716186.

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Souza, Lucas Airam C. de, Gustavo F. Camilo, Gabriel A. Fontes Rebello, Matteo Sammarco, Miguel Elias M. Campista, and Luís Henrique M. K. Costa. "ATHENA-FL: Evitando a Heterogeneidade Estatística através do Um-contra-Todos no Aprendizado Federado." In Workshop de Computação Urbana. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/courb.2023.717.

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O aprendizado federado é um novo paradigma que permite o treinamento de modelos de aprendizado de máquina através da colaboração entre clientes e um servidor de agregação. O treinamento dispensa o compartilhamento de dados privados, garantindo aos clientes privacidade de suas amostras. Entretanto, quando os clientes possuem distribuições de dados distintas, o treinamento apresenta dificuldades de convergência, resultando em erros preditivos no modelo final. Este artigo propõe um sistema de aprendizado federado que considera clientes com distribuições de dados heterogêneas e, mesmo assim, produ

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Arcanjo, Adenilton, and André Luiz Carvalho Ottoni. "Análise de Modelos de Aprendizado Profundo para Sistemas Embarcados aplicados à Classificação de Rachaduras em Construções." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2023. http://dx.doi.org/10.21528/cbic2023-067.

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A Inteligência Artificial (IA) tem sido aplicada em diversas áreas para otimizar tarefas, agilizar atividades e reduzir custos. Na construção civil, a IA tem sido utilizada atrav es´ do aprendizado de máquina e da visão computacional para automatizar a inspeção visual em obras e construções. Um dos principais usos e na detecção de patologias, como rachaduras, trincas e fissuras em estruturas como edifícios, pontes, tubulaçoes, e afins. Outra área que vem aplicando técnicas de aprendizado de máquina e a de sistemas embarcados. E possível encontrar cada vez mais dispositivos com menor poder de p

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