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Relevant bibliographies by topics / Improved Mobilenetv2

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Academic literature on the topic 'Improved Mobilenetv2'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Improved Mobilenetv2"

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Zhao, Liquan, Leilei Wang, Yanfei Jia, and Ying Cui. "A lightweight deep neural network with higher accuracy." PLOS ONE 17, no. 8 (2022): e0271225. http://dx.doi.org/10.1371/journal.pone.0271225.

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To improve accuracy of the MobileNet network, a new lightweight deep neural network is designed based on the MobileNetV2 network. Firstly, it modifies the network depth of MobileNetV2 to balance the image resolution, network width and depth to keep the gradient stable, which reduces the generation of gradient vanishing or gradient exploding. Secondly, it proposes an improved Bottleneck module by introducing channel attention mechanism. It assigns different weights for different channels according to the degree of relevance between the object features and channels. Therefore, the network can ex

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Li, Ping, Taiyu Han, Yifei Ren, Peng Xu, and Hongliu Yu. "Improved YOLOv4-tiny based on attention mechanism for skin detection." PeerJ Computer Science 9 (March 10, 2023): e1288. http://dx.doi.org/10.7717/peerj-cs.1288.

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Background An automatic bathing robot needs to identify the area to be bathed in order to perform visually-guided bathing tasks. Skin detection is the first step. The deep convolutional neural network (CNN)-based object detection algorithm shows excellent robustness to light and environmental changes when performing skin detection. The one-stage object detection algorithm has good real-time performance, and is widely used in practical projects. Methods In our previous work, we performed skin detection using Faster R-CNN (ResNet50 as backbone), Faster R-CNN (MobileNetV2 as backbone), YOLOv3 (Da

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Zungu, Ntandoyenkosi, Peter Olukanmi, and Pitshou Bokoro. "SynthSecureNet: An Improved Deep Learning Architecture with Application to Intelligent Violence Detection." Algorithms 18, no. 1 (2025): 39. https://doi.org/10.3390/a18010039.

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We present a new deep learning architecture, named SynthSecureNet, which hybridizes two popular architectures: MobileNetV2 and ResNetV2. The latter have been shown to be promising in violence detection. The aim of our architecture is to harness the combined strengths of the two known methods for improved accuracy. First, we leverage the pre-trained weights of MobileNetV2 and ResNet50V2 to initialize the network. Next, we fine-tune the network by training it on a dataset of labeled surveillance videos, with a focus on optimizing the fusion process between the two architectures. Experimental res

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Ma, Rui, Jia Wang, Wei Zhao, et al. "Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM." Agriculture 13, no. 1 (2022): 11. http://dx.doi.org/10.3390/agriculture13010011.

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Seeds are the most fundamental and significant production tool in agriculture. They play a critical role in boosting the output and revenue of agriculture. To achieve rapid identification and protection of maize seeds, 3938 images of 11 different types of maize seeds were collected for the experiment, along with a combination of germ and non-germ surface datasets. The training set, validation set, and test set were randomly divided by a ratio of 7:2:1. The experiment introduced the CBAM (Convolutional Block Attention Module) attention mechanism into MobileNetV2, improving the CBAM by replacing

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Hu, Qiming, Jianping Wang, Guo Zhang, and Jianhui Jin. "Space-Time Image Velocimetry Based on Improved MobileNetV2." Electronics 12, no. 2 (2023): 399. http://dx.doi.org/10.3390/electronics12020399.

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Space-time image velocimetry (STIV) technology has achieved good performance in river surface-flow velocity measurement, but the application in a field environment is affected by bad weather or lighting conditions, which causes large measurement errors. To improve the measurement accuracy and robustness of STIV, we combined STIV with deep learning. Additionally, considering the light weight of the neural network model, we adopted MobileNetV2 and improved its classification accuracy. We name this method MobileNet-STIV. We also constructed a sample-enhanced mixed dataset for the first time, with

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Aslamiah, Ayu Hidayah, Abdul Haris Rangkuti, Ayuliana Ayuliana, Varyl Hasbi Athala, Naufal Fauzi Lutffi, and Syaugi Vikri Aditama. "Improved Accuracy of Animal Skin Pattern retrieval with CNN Model and Distance Metrics." International Journal of Emerging Technology and Advanced Engineering 12, no. 2 (2022): 145–52. http://dx.doi.org/10.46338/ijetae0222_17.

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This study describes the retrieval of animal skins with very diverse shapes and textures. With so many variations in each animal skin pattern, an appropriate and supported CNN model is needed as well as an appropriate distance matrix method to support retrieval performance. This research was conducted on 6 types of animal skin images. In experimenting to obtain this animal skin image, 4 CNN models were used, namely EfficientnetB7, Inception V3, MobilenetV2, and Resnet50 V2, as well as 2 distance metrics methods, namely Euclidean and Manhattan. Based on the experiment, the average with 2 measur

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Tanwar, Neha, and Anil V. Turukmane. "Modified MobileNetV2 transfer learning model to detect road potholes." PeerJ Computer Science 11 (January 21, 2025): e2519. https://doi.org/10.7717/peerj-cs.2519.

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Road damage often includes potholes, cracks, lane degradation, and surface shading. Potholes are a common problem in pavements. Detecting them is crucial for maintaining infrastructure and ensuring public safety. A thorough assessment of pavement conditions is required before planning any preventive repairs. Herein, we report the use of transfer learning and deep learning (DL) models to preprocess digital images of pavements for better pothole detection. Fourteen models were evaluated, including MobileNet, MobileNetV2, NASNetMobile, DenseNet121, DenseNet169, InceptionV3, DenseNet201, ResNet152

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Lu, Jianbo, Xiaobin Liu, Xiaoya Ma, Jin Tong, and Jungui Peng. "Improved MobileNetV2 crop disease identification model for intelligent agriculture." PeerJ Computer Science 9 (September 25, 2023): e1595. http://dx.doi.org/10.7717/peerj-cs.1595.

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Using intelligent agriculture is an important way for the industry to achieve high-quality development. To improve the accuracy of the identification of crop diseases under conditions of limited computing resources, such as in mobile and edge computing, we propose an improved lightweight MobileNetV2 crop disease identification model. In this study, MobileNetV2 is used as the backbone network for the application of an improved Bottleneck structure. First, the number of operation channels is reduced using point-by-point convolution, the number of parameters of the model is reduced, and the re-pa

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Pang, Yue, Wenbo Yu, Yongan Zhang, Chuanzhong Xuan, and Pei Wu. "Sheep face recognition and classification based on an improved MobilenetV2 neural network." International Journal of Advanced Robotic Systems 20, no. 1 (2023): 172988062311529. http://dx.doi.org/10.1177/17298806231152969.

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Large-scale sheep farming has conventionally relied on barcodes and ear tags, devices that can be difficult to implement and maintain, for sheep identification and tracking. Biological data have also been used for tracking in recent years but have not been widely adopted due to the difficulty and high costs of data collection. To address these issues, a noncontact facial recognition technique is proposed in this study, in which training data were acquired in natural conditions using a series of video cameras, as Dupo sheep walked freely through a gate. A key frame extraction algorithm was then

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A, Kala, Sharon Femi P, Rajalakshmi V, Kalavathi S, and Ashwini K. "Face Mask Detection and Social Distance Monitoring with Deep Learning." Indian Journal of Science and Technology 16, no. 25 (2023): 1888–97. https://doi.org/10.17485/IJST/v16i25.70.

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Abstract <strong>Objectives:</strong> This work proposes a real-time classification model that can accurately detect whether an individual is wearing a face mask and maintaining social distance with the goal of developing a lightweight and easily deployable system for surveillance purposes. <strong>Methods:</strong> The proposed method easily identifies the human by bounding boxes and wearing of face mask by realtime Face Detection Recognition System. This is a robust model that involves detection, tracking and validation as its features. Pre trained deep learning models like In

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Dissertations / Theses on the topic "Improved Mobilenetv2"

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Sheng-HongChen and 陳聖弘. "Building Recognition Using Improved MobileNetV2 with One-Dimensional Linearwise Filters and Modified Focal Loss." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/4n6y88.

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Book chapters on the topic "Improved Mobilenetv2"

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Cheng, Zhiyu, Jun Liu, and Jinfeng Zhang. "An Improved Mobilenetv2 for Rust Detection of Angle Steel Tower Bolts Based on Small Sample Transfer Learning." In Intelligent Computing Methodologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13832-4_13.

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Zhu, Wenhui, Peijie Qiu, Xiwen Chen, et al. "Beyond MobileNet: An Improved MobileNet for Retinal Diseases." In Myopic Maculopathy Analysis. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54857-4_5.

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Nan, Jing, Xiyu Lei, Xiaoyu Yang, Yifan Chang, and Zhiguo Wang. "Food Classification Model Based on Improved MobileNetV3." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9243-0_29.

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Chen, Xi, Miaoyun Hu, Xinle Zou, and Yate Tan. "Employee Emotion Recognition Method Based on Improved MobileNetV3." In Advances in Economics, Business and Management Research. Atlantis Press International BV, 2024. https://doi.org/10.2991/978-94-6463-638-3_18.

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Zhou, Kaijun, Kaiwen Deng, Peng Chen, and Yiliang Hu. "An Improved Lightweight Network Based on MobileNetV3 for Palmprint Recognition." In Pattern Recognition and Computer Vision. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18907-4_58.

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Fang, Tong, Baoshuai Du, Yunjia Xue, Guang Yang, and Jingbo Zhao. "A Lightweight Target Detection Algorithm Based on Improved MobileNetv3-YOLOv3." In Knowledge Science, Engineering and Management. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10989-8_35.

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Yan, Qian, Yifei Chen, and Caicong Wu. "Identification of Plant Nutrient Deficiency Based on Improved MobileNetV3-Large Model." In Proceedings of 2023 Chinese Intelligent Automation Conference. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6187-0_40.

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Yao, Tao, Qi Zhang, Xingyu Wu, and Xiuyue Lin. "Image Recognition Method of Defective Button Battery Base on Improved MobileNetV1." In Image and Graphics Technologies and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-6033-4_24.

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Jin, Ronglu, Yidong Xu, Wei Xue, Beiming Li, Yingwei Yang, and Wenjian Chen. "An Improved Mobilenetv3-Yolov5 Infrared Target Detection Algorithm Based on Attention Distillation." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94551-0_22.

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An, Yulei, Jialin Tang, and Yongfeng Li. "A MobileNet SSDLite Model with Improved FPN for Forest Fire Detection." In Image and Graphics Technologies and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5096-4_20.

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Conference papers on the topic "Improved Mobilenetv2"

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Zhang, Ying, Pingshu Ge, Tao Zhang, and Yang Wang. "Motor Bearing Fault Diagnosis based on Improved MobilenetV2." In 2024 6th International Conference on Electronic Engineering and Informatics (EEI). IEEE, 2024. http://dx.doi.org/10.1109/eei63073.2024.10696838.

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Zhou, Zheng, Kaoru Hirota, Yaping Dai, Bemnet Wondimagegnehu Mersha, Wei Dai, and Yumin Lin. "A student behavior recognition algorithm based on improved MobileNetV2." In The 8th International Conference on Video and Image Processing, edited by Xuefeng Liang. SPIE, 2025. https://doi.org/10.1117/12.3059118.

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Yu, Yali, and Chengxun Zhang. "Facial Expression Recognition Based on Improved MobileNetV2 Network Model." In 2024 5th International Conference on Computers and Artificial Intelligence Technology (CAIT). IEEE, 2024. https://doi.org/10.1109/cait64506.2024.10962869.

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Almubarok, Wildan, Ulla Delfana Rosiani, and Rosa Andrie Asmara. "MobileNetV2 Pruning for Improved Efficiency in Catfish Classification on Resource-Limited Devices." In 2024 IEEE 10th Information Technology International Seminar (ITIS). IEEE, 2024. https://doi.org/10.1109/itis64716.2024.10845279.

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Hidayat, Rahmat, Achmad Solichin, Adhitia Erfina, Sudin Saepudin, Muhamad Muslih, and Mohd Zainuri Saringat. "Implementation of Malaria Detection Using MobileNetV2 Pretrained Model for Improved Deep Learning Based Diagnostics." In 2024 10th International Conference on Computing, Engineering and Design (ICCED). IEEE, 2024. https://doi.org/10.1109/icced64257.2024.10983858.

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Cao, Shuai, and Fang Li. "Pig Detection Method Based on Improved YOLOv8 and MobileNetV3." In 2024 International Symposium on Digital Home (ISDH). IEEE, 2024. https://doi.org/10.1109/isdh64927.2024.00021.

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Zhang, Yang. "Improved RT-DETR Based on MobileNetV4 for Vehicle Detection." In 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE). IEEE, 2025. https://doi.org/10.1109/icaace65325.2025.11019497.

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Yang, Xiaoteng, Lei Liu, Xifei Song, et al. "An Efficient Lightweight Satellite Image Classification Model with Improved MobileNetV3." In IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2024. http://dx.doi.org/10.1109/infocomwkshps61880.2024.10620744.

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Wei, Ling, Zongbao Liang, Juncai Wang, and Hao Liang. "Improved MobileNetV3 for Weather Scene Classification in Drone Aerial Video Imagery." In 2024 7th International Conference on Computer Information Science and Application Technology (CISAT). IEEE, 2024. http://dx.doi.org/10.1109/cisat62382.2024.10695376.

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Rani, Ritu, and Gotte Ranjith Kumar. "Automated Guava Disease Detection Using Fine-Tuned MobileNet for Improved Agricultural Productivity." In 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. IEEE, 2025. https://doi.org/10.1109/otcon65728.2025.11070618.

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