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Journal articles on the topic 'Improved Mobilenetv2'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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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Aghamohammadesmaeilketabforoosh, Kimia, Soodeh Nikan, Giorgio Antonini, and Joshua M. Pearce. "Optimizing Strawberry Disease and Quality Detection with Vision Transformers and Attention-Based Convolutional Neural Networks." Foods 13, no. 12 (2024): 1869. http://dx.doi.org/10.3390/foods13121869.

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Machine learning and computer vision have proven to be valuable tools for farmers to streamline their resource utilization to lead to more sustainable and efficient agricultural production. These techniques have been applied to strawberry cultivation in the past with limited success. To build on this past work, in this study, two separate sets of strawberry images, along with their associated diseases, were collected and subjected to resizing and augmentation. Subsequently, a combined dataset consisting of nine classes was utilized to fine-tune three distinct pretrained models: vision transfor

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Sun, Zhengzhi, Mayire Ibrayim, and Askar Hamdulla. "Detection of Pine Wilt Nematode from Drone Images Using UAV." Sensors 22, no. 13 (2022): 4704. http://dx.doi.org/10.3390/s22134704.

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Pine wilt nematode disease is a devastating forest disease that spreads rapidly. Using drone remote sensing to monitor pine wilt nematode trees promptly is an effective way to control the spread of pine wilt nematode disease. In this study, the YOLOv4 algorithm was used to automatically identify abnormally discolored wilt from pine wilt nematode disease on UAV remote sensing images. Because the network structure of YOLOv4 is too complex, although the detection accuracy is high, the detection speed is relatively low. To solve this problem, the lightweight deep learning network MobileNetv2 is us

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Jain, Mriga, and Brajesh Kumar Singh. "Leveraging Lightweight Pretrained Model for Brain Tumour Detection." BIO Web of Conferences 65 (2023): 05051. http://dx.doi.org/10.1051/bioconf/20236505051.

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This study presents an analysis of two deep learning models deployed for brain tumour detection: the lightweight pretrained MobileNetV2 and a novel hybrid model by combining light-weight MobileNetV2 with VGG16. The aim is to investigate the performance and efficiency of these models in terms of accuracy and training time. The new hybrid model integrates the strengths of both architectures, leveraging the depth-wise separable convolutions of MobileNetV2 and the deeper feature extraction capabilities of VGG16. Through experimentation and evaluation using a publicly available benchmark brain tumo

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Yang, Le, Huibin Long, Xiaoyun Yu, Huanhuan Zhang, Shuang Xu, and Yingwen Zhu. "Improved model for identifying rice panicle disease based on MobileNetV2." International Journal of Wireless and Mobile Computing 28, no. 3 (2025): 264–72. https://doi.org/10.1504/ijwmc.2025.145469.

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Moustafa, Akhin. "Comparative Analysis of Machine Learning, CNN, and Transfer Learning Models for Strawberry Leaf Health Classification." Zanin Journal of Science and Engineering 1, no. 2 (2025): 32–42. https://doi.org/10.64362/zjse.47.

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This study explores binary classification of strawberry leaf health (healthy vs. scorched leaf) using the Plant Village dataset. It compares traditional machine learning methods (SVM, k-NN, XGBoost), a custom CNN, and transfer learning models (ResNet50, MobileNetV2, VGG19). XGBoost performed best among traditional models (accuracy 0.94), while the CNN achieved 0.89 after addressing class imbalance. MobileNetV2 and VGG19 reached top accuracies of 1.0 and 0.99 with frozen layers. ResNet50 improved from 0.73 to 0.99 when fine-tuned. The results show lightweight models like MobileNetV2 and VGG19 a

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El-Shahat, Doaa, Ahmed Tolba, and Amit Krishan Kumar. "An Improved Deep Learning Model for Detecting Rice Diseases." Optimization in Agriculture 1 (January 10, 2024): 1–10. http://dx.doi.org/10.61356/j.oia.2024.1194.

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Early detection of rice plant diseases could help to quickly eradicate numerous diseases, such as fungi, viruses, and bacteria, consequently increasing rice yield. Traditional techniques for performing this task may not be the best because they take a long time, require experienced personnel, and are susceptible to a variety of infections. As a result, machine learning and deep learning approaches have recently been utilized to overcome these issues and present a more accurate model for detecting rice plant diseases. However, the current machine learning (ML) and deep learning (DL) models for

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Ding, Kai, Zhangqi Niu, Jizhuang Hui, Xueliang Zhou, and Felix T. S. Chan. "A Weld Surface Defect Recognition Method Based on Improved MobileNetV2 Algorithm." Mathematics 10, no. 19 (2022): 3678. http://dx.doi.org/10.3390/math10193678.

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Traditional welding quality inspection methods for pipelines and pressure vessels are time-consuming, labor-intensive, and suffer from false and missed inspection problems. With the development of smart manufacturing, there is a need for fast and accurate in-situ inspection of welding quality. Therefore, detection models with higher accuracy and lower computational complexity are required for technical support. Based on that, an in-situ weld surface defect recognition method is proposed in this paper based on an improved lightweight MobileNetV2 algorithm. It builds a defect classification mode

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Li, Tongkai, Huamao Huang, Yangyang Peng, Hui Zhou, Haiying Hu, and Ming Liu. "Quality Grading Algorithm of Oudemansiella raphanipes Based on Transfer Learning and MobileNetV2." Horticulturae 8, no. 12 (2022): 1119. http://dx.doi.org/10.3390/horticulturae8121119.

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As a traditional edible and medicinal fungus in China, Oudemansiella raphanipes has high economic benefits. In order to achieve the automatic classification of Oudemansiella raphanipes into four quality levels using their image dataset, a quality grading algorithm based on neural network models was proposed. At first, the transfer learning strategy and six typical convolution neural network models, e.g., VGG16, ResNet50, InceptionV3, NasNet-Mobile, EfficientNet, and MobileNetV2, were used to train the datasets. Experiments show that MobileNetV2 has good performance considering both testing acc

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胡, 江婧. "Otitis Media Image Classification and Recognition Model Based on Improved MobileNetV2." Modeling and Simulation 13, no. 02 (2024): 1814–29. http://dx.doi.org/10.12677/mos.2024.132170.

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Roh, Joo-Hyung, Se-Hong Min, and Min-Suk Kong. "Analysis of Fire Prediction Performance of Image Classification Models based on Convolutional Neural Network." Fire Science and Engineering 36, no. 6 (2022): 70–77. http://dx.doi.org/10.7731/kifse.9e906e7a.

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In this study, fire prediction performance was analyzed using convolutional neural network (CNN)-based classification models such as MobileNetV2, ResNet101, and EfficientNetB0 applicable to an edge computing-based fire detection system for improving fire safety. The fire prediction performance was evaluated using the performance evaluation measures including accuracy, recall, precision, F1-score, and the confusion matrix. The model size and inference time were assessed in terms of the light-weight classification model for the practical deployment and use. The analysis results confirmed that th

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Lan, Yubin, Kanghua Huang, Chang Yang, et al. "Real-Time Identification of Rice Weeds by UAV Low-Altitude Remote Sensing Based on Improved Semantic Segmentation Model." Remote Sensing 13, no. 21 (2021): 4370. http://dx.doi.org/10.3390/rs13214370.

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Real-time analysis of UAV low-altitude remote sensing images at airborne terminals facilitates the timely monitoring of weeds in the farmland. Aiming at the real-time identification of rice weeds by UAV low-altitude remote sensing, two improved identification models, MobileNetV2-UNet and FFB-BiSeNetV2, were proposed based on the semantic segmentation models U-Net and BiSeNetV2, respectively. The MobileNetV2-UNet model focuses on reducing the amount of calculation of the original model parameters, and the FFB-BiSeNetV2 model focuses on improving the segmentation accuracy of the original model.

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Shen, Xing, and Xiukun Wei. "A Real-Time Subway Driver Action Sensoring and Detection Based on Lightweight ShuffleNetV2 Network." Sensors 23, no. 23 (2023): 9503. http://dx.doi.org/10.3390/s23239503.

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The driving operations of the subway system are of great significance in ensuring the safety of trains. There are several hand actions defined in the driving instructions that the driver must strictly execute while operating the train. The actions directly indicate whether equipment is normally operating. Therefore, it is important to automatically sense the region of the driver and detect the actions of the driver from surveillance cameras to determine whether they are carrying out the corresponding actions correctly or not. In this paper, a lightweight two-stage model for subway driver actio

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Wang, Wenjie, Mengling He, Xiaohua Wang, Jianwei Ma, and Huajian Song. "Medical Gesture Recognition Method Based on Improved Lightweight Network." Applied Sciences 12, no. 13 (2022): 6414. http://dx.doi.org/10.3390/app12136414.

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Surgery is a compelling application field for collaborative control robots. This paper proposes a gesture recognition method applied to a medical assistant robot delivering instruments to collaborate with surgeons to complete surgeries. The key to assisting the surgeon in passing instruments in the operating room is the ability to recognize the surgeon’s hand gestures accurately and quickly. Existing gesture recognition techniques suffer from poor recognition accuracy and low rate. To address the existing shortcomings, we propose an improved lightweight convolutional neural network called E-Mo

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Li Xueling, 李雪玲, 禹静 Yu Jing, 张海洋 Zhang Haiyang та ін. "改进注意力机制MobileNetV2网络对水污染SERS分类研究". Laser & Optoelectronics Progress 62, № 7 (2025): 0730004. https://doi.org/10.3788/lop242165.

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Shaimaa Saadoon Mahmood ALrfae, Et al. "An Innovative Deep Learning Method to Diagnose Mosquito-Borne Illnesses in Blood Image Analysis." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1655–62. http://dx.doi.org/10.17762/ijritcc.v11i10.8727.

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Introduction: Malaria, an infectious illness carried by the bite of infected mosquitoes, is a significant public health concern, especially in Africa. The management of mosquito-human contact is crucial to mitigate its transmission. Artificial intelligence, including machine learning and deep learning techniques, is being utilized to enhance the diagnosis and identification of mosquito species. This advancement aims to facilitate the development of more efficient control measures. Aims and Objective: To analyze the efficiency of three deep learning models in identifying blood-borne disea

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Sun, Haijing, Wen Zhou, Jiapeng Yang, et al. "An Improved Medical Image Classification Algorithm Based on Adam Optimizer." Mathematics 12, no. 16 (2024): 2509. http://dx.doi.org/10.3390/math12162509.

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Due to the complexity and illegibility of medical images, it brings inconvenience and difficulty to the diagnosis of medical personnel. To address these issues, an optimization algorithm called GSL(Gradient sine linear) based on Adam algorithm improvement is proposed in this paper, which introduces gradient pruning strategy, periodic adjustment of learning rate, and linear interpolation strategy. The gradient trimming technique can scale the gradient to prevent gradient explosion, while the periodic adjustment of the learning rate and linear interpolation strategy adjusts the learning rate acc

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Yan, Chunyu, Zhonghui Chen, Zhilin Li, et al. "Tea Sprout Picking Point Identification Based on Improved DeepLabV3+." Agriculture 12, no. 10 (2022): 1594. http://dx.doi.org/10.3390/agriculture12101594.

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Tea sprout segmentation and picking point localization via machine vision are the core technologies of automatic tea picking. This study proposes a method of tea segmentation and picking point location based on a lightweight convolutional neural network named MC-DM (Multi-Class DeepLabV3+ MobileNetV2 (Mobile Networks Vision 2)) to solve the problem of tea shoot picking point in a natural environment. In the MC-DM architecture, an optimized MobileNetV2 is used to reduce the number of parameters and calculations. Then, the densely connected atrous spatial pyramid pooling module is introduced int

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Khan, Arshi, and S. D. Samantaray. "Comparative Analysis of Pre-Trained CNN Architectures for Apple Foliar Disease Classification." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem25943.

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Apple foliar diseases can significantly impact crop yield and quality. Early and accurate disease detection allows for timely disease management, reducing losses. This paper presents a comparative analysis of pretrained Convolutional neural network (CNN) architectures for automated apple foliar disease classification from leaf images. Eight CNN models, including DenseNet121, DenseNet201, ResNet50V2, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2 and Xception, were evaluated using an apple leaf dataset with five disease classes - scab, rust, black rot, multiple diseases, and healthy. Mo

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Peng, Longguang, Jicheng Zhang, Yuanqi Li, and Guofeng Du. "A novel percussion-based approach for pipeline leakage detection with improved MobileNetV2." Engineering Applications of Artificial Intelligence 133 (July 2024): 108537. http://dx.doi.org/10.1016/j.engappai.2024.108537.

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Pu, Ying, Yaqin Zhao, Hao Ma, and Junxiong Wang. "A Lightweight Pig Aggressive Behavior Recognition Model by Effective Integration of Spatio-Temporal Features." Animals 15, no. 8 (2025): 1159. https://doi.org/10.3390/ani15081159.

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With the rise of smart agriculture and the expansion of pig farming, pig aggressive behavior recognition is crucial for maintaining herd health and improving farming efficiency. The differences in background and light variation in different barns can lead to the missed detection and false detection of pig aggressive behaviors. Therefore, we propose a deep learning-based pig aggressive behavior recognition model, in order to improve the adaptability of the model in complex pig environments. This model, combined with MobileNetV2 and Autoformer, can effectively extract local detail features of pi

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Cao, Zhenjiang, and Zhenhai Cao. "Design of a MobilNetV2-Based Retrieval System for Traditional Cultural Artworks." International Journal of Gaming and Computer-Mediated Simulations 16, no. 1 (2023): 1–17. http://dx.doi.org/10.4018/ijgcms.334700.

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Aiming at the problem that it is difficult for art teachers to take into account each student in the art appreciation education in colleges and universities, this paper proposes a retrieval system for traditional cultural works of art. Dense connections are used to replace residual connections between bottlenecks in MobileNetV2 network and gradient transmission in the network. The dilution factor is used to control the size of the network to solve the problem of the rapid increase in the number of network channels. In addition, the non-local attention mechanism is effectively combined with the

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Ren, Yong, Dong Liu, and Sanhong Gu. "A lightweight defect classification method for latex gloves based on image enhancement." Computer Science and Information Systems, no. 00 (2025): 7. https://doi.org/10.2298/csis240911007r.

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This paper presents a glove defect classification method that integrates image enhancement techniques with a lightweight model to enhance the efficiency and accuracy of glove defect classification in industrial manufacturing. A dataset comprising images of five types of gloves was collected, totaling 360 sample images, for the training and validation of a deep learning-based glove defect classification model. Image enhancement techniques, including super-pixels, exposure adjustment, blurring, and limited contrast adaptive histogram equalization, increased dataset diversity and size, improving

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Chen, Yuang, Yong Li, Shaohua Li, Shuhan Lv, and Fang Lin. "DualCascadeTSF-MobileNetV2: A Lightweight Violence Behavior Recognition Model." Applied Sciences 15, no. 7 (2025): 3862. https://doi.org/10.3390/app15073862.

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This paper proposes a lightweight violent behavior recognition model, DualCascadeTSF-MobileNetV2, which is improved based on the temporal shift module and its subsequent research. By introducing the Dual Cascade Temporal Shift and Fusion module, the model further enhances the feature correlation ability in the time dimension and solves the problem of information sparsity caused by multiple temporal shifts. Meanwhile, the model incorporates the efficient lightweight structure of MobileNetV2, significantly reducing the number of parameters and computational complexity. Experiments were conducted

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Islam, Rakibul, Amatul Bushra Akhi, and Farzana Akter. "A fine tune robust transfer learning based approach for brain tumor detection using VGG-16." Bulletin of Electrical Engineering and Informatics 12, no. 6 (2023): 3861–68. http://dx.doi.org/10.11591/eei.v12i6.5646.

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Brain tumor recognition by magnetic resonance imaging (MRI) is crucial because it improves survival rates and allows them to plan treatments accordingly. An accumulation of abnormal cells known as a brain tumor can spread to nearby tissues and endanger the patient. Magnetic resonance imagery is the primary imaging technique which determines the extent of brain tumors. Deep learning techniques rapidly grew in computer vision due to ample data for model training and improved designs on applications. MRI has shown promising results when using deep learning approaches to identify and classify brai

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Soongswang, Kritpawit, and Chantana Chantrapornchai. "Accelerating automatic model finding with layer replications case study of MobileNetV2." PLOS ONE 19, no. 8 (2024): e0308852. http://dx.doi.org/10.1371/journal.pone.0308852.

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In this paper, we propose a method to reduce the model architecture searching time. We consider MobileNetV2 for 3D face recognition tasks as a case study and introducing the layer replication to enhance accuracy. For a given network, various layers can be replicated, and effective replication can yield better accuracy. Our proposed algorithm identifies the optimal layer replication configuration for the model. We considered two acceleration methods: distributed data-parallel training and concurrent model training. Our experiments demonstrate the effectiveness of the automatic model finding pro

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Peng, Nengtian, Ming Chen, and Guofu Feng. "Feature Extraction and Recognition of Chinese Mitten Crab Carapace Based on Improved MobileNetV2." Applied Sciences 14, no. 12 (2024): 4982. http://dx.doi.org/10.3390/app14124982.

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The Chinese mitten crab (Eriocheir sinensis), a species unique to Chinese aquaculture, holds significant economic value in the seafood market. In response to increasing concerns about the quality and safety of Chinese mitten crab products, the high traceability costs, and challenges for consumers in verifying the authenticity of individual crabs, this study proposes a lightweight individual recognition model for Chinese mitten crab carapace images based on an improved MobileNetV2. The method first utilizes a lightweight backbone network, MobileNetV2, combined with a coordinate attention mechan

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严, 春雨. "Tea Disease Identification Method Based on Improved MobileNet V2." Software Engineering and Applications 11, no. 04 (2022): 743–50. http://dx.doi.org/10.12677/sea.2022.114077.

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Li, Peibo, Jiangwu Zhou, and Xiaohua Xu. "Real-Time Image Semantic Segmentation Based on Improved DeepLabv3+ Network." Big Data and Cognitive Computing 9, no. 6 (2025): 152. https://doi.org/10.3390/bdcc9060152.

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To improve the performance of the image semantic segmentation algorithm and make the algorithm achieve a better balance between accuracy and real-time performance when segmenting images, this paper proposes a real-time image semantic segmentation model based on an improved DeepLabv3+ network. First, the MobileNetV2 model with less computational overhead and number of parameters is selected as the backbone network to improve the segmentation speed; then, the Feature Enhancement Module (FEM) is introduced to several shallow features with different scale sizes in MobileNetV2, and then these shall

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Mukhlif, Abdulrahman Abbas, Belal Al-Khateeb, and Mazin Abed Mohammed. "Incorporating a Novel Dual Transfer Learning Approach for Medical Images." Sensors 23, no. 2 (2023): 570. http://dx.doi.org/10.3390/s23020570.

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Recently, transfer learning approaches appeared to reduce the need for many classified medical images. However, these approaches still contain some limitations due to the mismatch of the domain between the source domain and the target domain. Therefore, this study aims to propose a novel approach, called Dual Transfer Learning (DTL), based on the convergence of patterns between the source and target domains. The proposed approach is applied to four pre-trained models (VGG16, Xception, ResNet50, MobileNetV2) using two datasets: ISIC2020 skin cancer images and ICIAR2018 breast cancer images, by

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Fang, Lifa, Yanqiang Wu, Yuhua Li, et al. "Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network." Agronomy 11, no. 11 (2021): 2328. http://dx.doi.org/10.3390/agronomy11112328.

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A consistent orientation of ginger shoots when sowing ginger is more conducive to high yields and later harvesting. However, current ginger sowing mainly relies on manual methods, seriously hindering the ginger industry’s development. Existing ginger seeders still require manual assistance in placing ginger seeds to achieve consistent ginger shoot orientation. To address the problem that existing ginger seeders have difficulty in automating seeding and ensuring consistent ginger shoot orientation, this study applies object detection techniques in deep learning to the detection of ginger and pr

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Nallapa Reddy, Venkata Sai Swaroop Reddy. "Improved Pancreatic Cancer Diagnosis: Deep Learning Integration with U-Net for Segmented Histopathology Image Analysis." International Journal of Computing and Engineering 7, no. 1 (2025): 1–15. https://doi.org/10.47941/ijce.2483.

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Pancreatic cancer remains one of the most lethal malignancies due to its asymptomatic early stages and rapid progression, leading to delayed diagnosis and limited treatment options. Accurate and early detection is critical for improving patient outcomes. This study introduces a robust deep learning approach integrating U-Net for image segmentation and four state-of-the-art Convolutional Neural Network (CNN) models—ResNet50, VGG16, MobileNetV2, and DenseNet121—for the classification of pancreatic cancer histopathology images. To address the challenges of data scarcity, various data augmentation

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Li, Xianguang, Jia Wei, Aoxiang Jia, and Jianpu Lin. "P‐3.11: An Improved SSD Algorithm." SID Symposium Digest of Technical Papers 56, S1 (2025): 905–8. https://doi.org/10.1002/sdtp.18961.

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Compared with Fast R‐CNN[1], YOLO[2‐4] and other detection algorithms[5], SSD algorithm[6] has better detection accuracy and real‐time performance detection performance. However, the comprehensive detection performance of SSD algorithm is strong, but it still has some shortcomings in network structure, model training and other aspects. Therefore, aiming at the problems of large network calculation, low multi‐target detection accuracy and unbalanced feature learning in the application of SSD algorithm to multi‐target detection and classification, this paper proposes an Adaptive Feature Fusion (

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Yin, Chenghai, Tiwei Zeng, Huiming Zhang, Wei Fu, Lei Wang, and Siyu Yao. "Maize Small Leaf Spot Classification Based on Improved Deep Convolutional Neural Networks with a Multi-Scale Attention Mechanism." Agronomy 12, no. 4 (2022): 906. http://dx.doi.org/10.3390/agronomy12040906.

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Maize small leaf spot (Bipolaris maydis) is one of the most important diseases of maize. The severity of the disease cannot be accurately identified, the cost of pesticide application increases every year, and the agricultural ecological environment is polluted. Therefore, in order to solve this problem, this study proposes a novel deep learning network DISE-Net. We designed a dilated-inception module instead of the traditional inception module for strengthening the performance of multi-scale feature extraction, then embedded the attention module to learn the importance of interchannel relatio

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Dou, Shiqing, Lin Wang, Donglin Fan, Linlin Miao, Jichi Yan, and Hongchang He. "Classification of Citrus Huanglongbing Degree Based on CBAM-MobileNetV2 and Transfer Learning." Sensors 23, no. 12 (2023): 5587. http://dx.doi.org/10.3390/s23125587.

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Citrus has become a pivotal industry for the rapid development of agriculture and increasing farmers’ incomes in the main production areas of southern China. Knowing how to diagnose and control citrus huanglongbing has always been a challenge for fruit farmers. To promptly recognize the diagnosis of citrus huanglongbing, a new classification model of citrus huanglongbing was established based on MobileNetV2 with a convolutional block attention module (CBAM-MobileNetV2) and transfer learning. First, the convolution features were extracted using convolution modules to capture high-level object-b

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MN, Musa, Badmos NO, Saidu IR, and Abdulrazaq U. "Protective face covering: An application of MobileNetV2 detector." International Research Journal of Science, Technology, Education, and Management 2, no. 1 (2022): 50–62. https://doi.org/10.5281/zenodo.6496755.

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COVID-19 has created a global serious health hazard with far-reaching consequences for society, our perceptions of the world, and how we live our daily lives. As a result, the World Health Organization recommended the use of face masks and social isolation to help reduce the rising number of infections. However, subsequent research has revealed that face masks alone can be ineffective, particularly in crowded settings or hospitals. Face shields can also be used in addition or as an alternative for face masks because they are indefinitely reusable and can be washed with soap and water or standa

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Wang, Fujie, Fanfan Li, Wei Sun, Xiaozhong Song, and Huishan Lu. "An Identification Method for Mixed Coal Vitrinite Components Based on An Improved DeepLabv3+ Network." Energies 17, no. 14 (2024): 3453. http://dx.doi.org/10.3390/en17143453.

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To address the high complexity and low accuracy issues of traditional methods in mixed coal vitrinite identification, this paper proposes a method based on an improved DeepLabv3+ network. First, MobileNetV2 is used as the backbone network to reduce the number of parameters. Second, an atrous convolution layer with a dilation rate of 24 is added to the ASPP (atrous spatial pyramid pooling) module to further increase the receptive field. Meanwhile, a CBAM (convolutional block attention module) attention mechanism with a channel multiplier of 8 is introduced at the output part of the ASPP module

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Ellen Flores Mangaoang. "Analysis of Deep Learning Algorithms for Grape Leaf Disease Detection." Journal of Information Systems Engineering and Management 10, no. 33s (2025): 336–44. https://doi.org/10.52783/jisem.v10i33s.5537.

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Plant leaf diseases are crucial in agriculture as they can affect food security. Grapes are one of the important fruits we consume for health considerations. This paper aims to investigate the various deep learning algorithms focused on plant disease detection. A systematic literature review was conducted to determine the top three deep learning algorithms to be utilized in this paper. It intends to compare the performance of the top three deep learning algorithms with appropriate performance metrics. The top three deep learning algorithms identified were Convolutional Neural Network, Efficien

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Xu, Xingmei, Yuqi Zhang, Hongcheng Cao, Dawei Yang, Lei Zhou, and Helong Yu. "Recognition of Edible Fungi Fruit Body Diseases Based on Improved ShuffleNetV2." Agronomy 13, no. 6 (2023): 1530. http://dx.doi.org/10.3390/agronomy13061530.

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Early recognition of fruit body diseases in edible fungi can effectively improve the quality and yield of edible fungi. This study proposes a method based on improved ShuffleNetV2 for edible fungi fruit body disease recognition. First, the ShuffleNetV2+SE model is constructed by deeply integrating the SE module with the ShuffleNetV2 network to make the network pay more attention to the target area and improve the model’s disease classification performance. Second, the network model is optimized and improved. To simplify the convolution operation, the 1 × 1 convolution layer after the 3 × 3 dep

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Faris, Muhammad, Endro Ariyanto, and Yogi Anggun Saloko Yudo. "IMPROVED REAL-TIME HOUSE FIRE DETECTION SYSTEM PERFORMANCE WITH IMAGE CLASSIFICATION USING MOBILENETV2 MODEL." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 8, no. 2 (2023): 656–63. http://dx.doi.org/10.29100/jipi.v8i2.3803.

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The problem with the Ardunio microcontroller-based fire detection system with fire and smoke sensors is the detection distance. For example, in another research, it was stated that the maximum distance for fire detection on two pieces of paper that were burned was 140 cm. This means that if the fire point is at a farther distance, the system cannot detect a fire early, of course, this will be problematic if used in a wider room. Based on these problems, a system is needed that can detect fires in large rooms. A method that can be used is detection using image classification. MobileNetV2 is a r

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Liao, Yun-Te, Chien-Hung Lee, Kuo-Su Chen, Chie-Pein Chen, and Tun-Wen Pai. "Data Augmentation Based on Generative Adversarial Networks to Improve Stage Classification of Chronic Kidney Disease." Applied Sciences 12, no. 1 (2021): 352. http://dx.doi.org/10.3390/app12010352.

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The prevalence of chronic kidney disease (CKD) is estimated to be 13.4% worldwide and 15% in the United States. CKD has been recognized as a leading public health problem worldwide. Unfortunately, as many as 90% of CKD patients do not know that they already have CKD. Ultrasonography is usually the first and the most commonly used imaging diagnostic tool for patients at risk of CKD. To provide a consistent assessment of the stage classifications of CKD, this study proposes an auxiliary diagnosis system based on deep learning approaches for renal ultrasound images. The system uses the ACWGAN-GP

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