Convolutional Neural Network Transfer Learning Research Articles

A comparative study of early stage Alzheimer’s disease classification using various transfer learning CNN frameworks

ABSTRACT The current research explores the improvements in predictive performance and computational efficiency that machine learning and deep learning methods have made over time. Specifically, the application of transfer learning concepts within Convolutional Neural Networks (CNNs) has proved useful for diagnosing and classifying the various stages of Alzheimer’s disease. Using base architectures such as Xception, InceptionResNetV2, DenseNet201, InceptionV3, ResNet50, and MobileNetV2, this study extends these models by adding batch normalization (BN), dropout, and dense layers. These enhancements improve the model’s effectiveness and precision in addressing the specified medical issue. The proposed model is rigorously validated and evaluated using publicly available Kaggle MRI Alzheimer’s data consisting of 1280 testing images and 5120 patient training images. For comprehensive performance evaluation, precision, recall, F1-score, and accuracy metrics are utilized. The findings indicate that the Xception method is the most promising of those considered. Without employing five K-fold techniques, this model obtains a 99% accuracy and 0.135 loss score. In addition, integrating five K-fold methods enhances the accuracy to 99.68% while decreasing the loss score to 0.120. The research further included the evaluation of the Receiver Operating Characteristic Area Under the Curve (ROC-AUC) for various classes and models. As a result, our model may detect and diagnose Alzheimer’s disease quickly and accurately.

Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network

Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network

Indonesian Fake News Classification Using Transfer Learning in CNN and LSTM

Fake news spreads quickly and is challenging to stop due to the ease of accessing and sharing information online. Deep learning techniques are a method that can be used to identify fake news quickly and accurately. The types of neural networks commonly utilized in deep learning architectures include Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), which can perform well when managing the task of classifying fake news, according to several pertinent studies. Regarding handling instances of Indonesian fake news classification, this study compares how well the CNN and LSTM models perform. However, given that Indonesian is a low-resource language with scant documentation, it is challenging to build an adequate data set. At the same time, the CNN and LSTM classification models require significant training data. We proposed a transfer learning method by combining two classification models with a pre-trained IndoBERT language model. 1340 news text data were used, including 643 actual news texts from CNN Indonesia, Liputan6, and Detik and 697 fake news texts from TurnBackHoax. As a result, the performance of the combination of the LSTM classification model with IndoBERT outperformed that of the CNN classification model with IndoBERT, which only produced an accuracy of 92.91%, down by 6%, and was able to produce an accuracy of up to 97.76%, an increase of 4.8% from before. Furthermore, the results show that the LSTM classification model outperforms the CNN classification model in capturing the representation created by IndoBERT. Additionally, these insights may serve as a basis for future research on identifying fake news in Indonesia, helping to improve methods for combatting misinformation in Indonesia.

Analysis of Convolutional Neural Network-based Image Classifications: A Multi-Featured Application for Rice Leaf Disease Prediction and Recommendations for Farmers

This study presents a novel method for improving rice disease classification using 8 different convolutional neural network (CNN) algorithms, which will further the field of precision agriculture. A thorough investigation of deep learning methods is carried out using the UCI dataset in order to create a reliable and effective model that can correctly identify a range of rice diseases. The suggested transfer learning models performs better at identifying subtle features and complex patterns in the dataset, which results in extremely accurate disease classification. Moreover, the study goes beyond the creation of models by incorporating an intuitive Tkinter-based application that offers farmers a feature-rich interface. With the help of this cutting-edge application, farmers will be able to make timely and well-informed decisions by enabling real-time disease prediction and providing personalized recommendations. Together with the user-friendly Tkinter interface, the smooth integration of cutting-edge CNN transfer Learning algorithms-based technology that include ResNet-50, InceptionV3, VGG16, MobileNetv2 with the UCI dataset represents a major advancement toward modernizing agricultural practices and guaranteeing sustainable crop management. Remarkable outcomes include 75% accuracy for ResNet-50, 90% accuracy for DenseNet121, 84% accuracy for VGG16, 95.83% accuracy for MobileNetV2, 91.61% accuracy for DenseNet169, and 86% accuracy for InceptionV3. These results give a concise summary of the models' capabilities, assisting researchers in choosing appropriate strategies for precise and successful rice crop disease identification. A severe overfitting has been seen on VGG19 with 70% accuracy and Nasnet with 80.02% accuracy. On Renset101 only an accuracy of 54% could be achieved along with only 33% on efficientNetB0. MobileNetV2 trained model was successfully deployed on a tkinter GUI application to make predictions using image or real time video capturing.

An Intelligent Mechanism to Detect Multi-Factor Skin Cancer.

Deep learning utilizing convolutional neural networks (CNNs) stands out among the state-of-the-art procedures in PC-supported medical findings. The method proposed in this paper consists of two key stages. In the first stage, the proposed deep sequential CNN model preprocesses images to isolate regions of interest from skin lesions and extracts features, capturing the relevant patterns and detecting multiple lesions. The second stage incorporates a web tool to increase the visualization of the model by promising patient health diagnoses. The proposed model was thoroughly trained, validated, and tested utilizing a database related to the HAM 10,000 dataset. The model accomplished an accuracy of 96.25% in classifying skin lesions, exhibiting significant areas of strength. The results achieved with the proposed model validated by evaluation methods and user feedback indicate substantial improvement over the current state-of-the-art methods for skin lesion classification (malignant/benign). In comparison to other models, sequential CNN surpasses CNN transfer learning (87.9%), VGG 19 (86%), ResNet-50 + VGG-16 (94.14%), Inception v3 (90%), Vision Transformers (RGB images) (92.14%), and the Entropy-NDOELM method (95.7%). The findings demonstrate the potential of deep learning, convolutional neural networks, and sequential CNN in disease detection and classification, eventually revolutionizing melanoma detection and, thus, upgrading patient consideration.

Skin-CAD: Explainable deep learning classification of skin cancer from dermoscopic images by feature selection of dual high-level CNNs features and transfer learning

Skin cancer (SC) significantly impacts many individuals' health all over the globe. Hence, it is imperative to promptly identify and diagnose such conditions at their earliest stages using dermoscopic imaging. Computer-aided diagnosis (CAD) methods relying on deep learning techniques especially convolutional neural networks (CNN) can effectively address this issue with outstanding outcomes. Nevertheless, such black box methodologies lead to a deficiency in confidence as dermatologists are incapable of comprehending and verifying the predictions that were made by these models. This article presents an advanced an explainable artificial intelligence (XAI) based CAD system named “Skin-CAD” which is utilized for the classification of dermoscopic photographs of SC. The system accurately categorises the photographs into two categories: benign or malignant, and further classifies them into seven subclasses of SC. Skin-CAD employs four CNNs of different topologies and deep layers. It gathers features out of a pair of deep layers of every CNN, particularly the final pooling and fully connected layers, rather than merely depending on attributes from a single deep layer. Skin-CAD applies the principal component analysis (PCA) dimensionality reduction approach to minimise the dimensions of pooling layer features. This also reduces the complexity of the training procedure compared to using deep features from a CNN that has a substantial size. Furthermore, it combines the reduced pooling features with the fully connected features of each CNN. Additionally, Skin-CAD integrates the dual-layer features of the four CNNs instead of entirely depending on the features of a single CNN architecture. In the end, it utilizes a feature selection step to determine the most important deep attributes. This helps to decrease the general size of the feature set and streamline the classification process. Predictions are analysed in more depth using the local interpretable model-agnostic explanations (LIME) approach. This method is used to create visual interpretations that align with an already existing viewpoint and adhere to recommended standards for general clarifications. Two benchmark datasets are employed to validate the efficiency of Skin-CAD which are the Skin Cancer: Malignant vs. Benign and HAM10000 datasets. The maximum accuracy achieved using Skin-CAD is 97.2 % and 96.5 % for the Skin Cancer: Malignant vs. Benign and HAM10000 datasets respectively. The findings of Skin-CAD demonstrate its potential to assist professional dermatologists in detecting and classifying SC precisely and quickly.

Research on sound quality of roller chain transmission system based on multi-source transfer learning

To establish the sound quality evaluation model of roller chain transmission system, we collect the running noise under different working conditions. After the noise samples are preprocessed, a group of experienced testers are organized to evaluate them subjectively. Mel frequency cepstral coefficient (MFCC) of each noise sample is calculated, and the MFCC feature map is used as an objective evaluation. Combining with the subjective and objective evaluation results of the roller chain system noise, we can get the original dataset of its sound quality research. However, the number of high-quality noise samples is relatively small. Based on the sound quality research of various chain transmission systems, a novel method called multi-source transfer learning convolutional neural network (MSTL-CNN) is proposed. By transferring knowledge from multiple source tasks to target task, the difficulty of small sample sound quality prediction is solved. Compared with the problem that single source task transfer learning has too much error on some samples, MSTL-CNN can give full play to the advantages of all transfer learning models. The results also show that the MSTL-CNN proposed in this paper is significantly better than the traditional sound quality evaluation methods.

Centrifugal Pump Fault Detection with Convolutional Neural Network Transfer Learning.

The centrifugal pump is the workhorse of many industrial and domestic applications, such as water supply, wastewater treatment and heating. While modern pumps are reliable, their unexpected failures may jeopardise safety or lead to significant financial losses. Consequently, there is a strong demand for early fault diagnosis, detection and predictive monitoring systems. Most prior work on machine learning-based centrifugal pump fault detection is based on either synthetic data, simulations or data from test rigs in controlled laboratory conditions. In this research, we attempted to detect centrifugal pump faults using data collected from real operational pumps deployed in various places in collaboration with a specialist pump engineering company. The detection was done by the binary classification of visual features of DQ/Concordia patterns with residual networks. Besides using a real dataset, this study employed transfer learning from the image detection domain to systematically solve a real-life problem in the engineering domain. By feeding DQ image data into a popular and high-performance residual network (e.g., ResNet-34), the proposed approach achieved up to 85.51% classification accuracy.

An investigation into real-time surface crack classification and measurement for structural health monitoring using transfer learning convolutional neural networks and Otsu method

This study introduces a pioneering system for real-time classification and measurement of concrete surface cracks, a crucial aspect of Structural Health Monitoring (SHM). We harness the power of transfer learning (TL) in Convolutional Neural Networks (CNNs), including renowned models such as MobileNetV2, EfficientNetV2, InceptionV3, and ResNet50. Notably, our model excels, particularly with TL MobileNetV2, achieving remarkable results – a 99.87% accuracy rate, 99.74% recall, 100% precision, and an impressive 99.87% F1-score. Incorporating the Otsu method for image segmentation, our system accurately assesses individual crack sizes. To refine measurements, Euclidean distance calculations and a 'pixel per inch' technique, accounting for video resolution, ensure millimeter-level width estimations. Precision is validated through manual experiments using a vernier caliper, specifically the Mitutoyo Absolute Digital Caliper. This tool ensures high accuracy, with an error margin of ±0.2 mm to ±0.3 mm, making it efficient for detailed measurements. Despite these promising outcomes, it is crucial to acknowledge inherent limitations. These include dependence on image quality, challenges in generalization, sensitivity to training data, assumption of linear crack width calculation, resolution dependency, and other factors. These limitations underscore the need for further refinement in our proposed classification model and measurement technique. This research represents a significant advancement in the SHM field, catering to early detection and timely maintenance requirements essential for infrastructure safety and longevity. As the field increasingly prioritizes rapid detection, our model presents a versatile solution that enhances the potential of Structural Health Monitoring.

Prediction of ash content in coal slime flotation based on CNN-BP method with residual estimation

ABSTRACT Ash content is a key metric for assessing coal slime flotation performance. Traditional methods of sampling, combustion, and weighing lead to measurement delays, thus image-based real-time ash prediction during flotation has become meaningful. However, the complex characteristics of the multiphase system in flotation pulp images make the extraction of an effective dataset for ash recognition challenging. This paper introduces an image-based method for predicting ash content in solid-liquid pulp mixtures using Convolutional Neural Networks (CNNs). A CNN transfer learning method based on ImageNet is proposed, combined with a Backpropagation (BP) residual estimation module with specified feature filters, reducing the sample size requirement. Four classic CNN networks were utilized. Results indicate that introducing the BP residual estimation module improves the CNN network’s prediction accuracy in small samples, with total residuals consistent with the white noise distribution. Notably, the 5% accuracy (absolute error less than 5%) of VGG19 without the CNN classic residual structure improved significantly, reflecting the superior residual estimation of BP and the robust performance of the CNN-BP in predicting pulp ash content. This research offers a new option for transitioning previous dataset collection methods for ash detection in the coal slime flotation process.

A Model for Detecting the Presence of Pesticide Residues in Edible Parts of Tomatoes, Cabbages, Carrots, and Green Pepper Vegetables

With increased resistant pests and low crop yields, farmers especially in sub-Saharan Africa have greatly embraced usage of chemicals. These chemicals include pesticides used in gardens for better yields and also in the stalls for longer shelf life by sellers of farm products especially fresh perishables like tomatoes, cabbages, carrots, and green pepper vegetables. This, if not checked, may expose humans and animals to pesticide residues. In this research, a model for detecting the presence of pesticide residues in edible parts of vegetables (tomatoes, cabbages, carrots, and green pepper) was developed. A dataset consisting of 1094 images of both contaminated and uncontaminated vegetables including tomatoes, cabbages, carrots, and green pepper with a scale magnification of 800 × 1276 pixels taken using InfiRay P2 pro Night Vision Go Mini Infrared Thermal camera with a thermal module was taken from different daily markets in Mbarara city, South Western Uganda. Image preprocessing was done by noise removal and grayscale conversion. Both the neural network and median filter were applied on the images. A python script was used to cluster the dataset based on chemical concentrations rates of 0.1–0.8 mg/kg, 0.9–1.3 mg/kg, and 1.4–1.7 mg/kg, and this was done for both training and testing dataset. Feature extraction was done to detect the presence of mancozeb, dioxacarb, and methidathion residues from the cleaned images. To test the developed model, convolutional neural networks transfer learning models, Inception V3, VGG16, VGG19, ResNet50, and the scratch model were used. From the results obtained, Inception V3 achieved better performance compared to other transfer learning models with 96.77% followed by VGG16 at 86.98%, VGG19 at 87.56%, and ResNet50 at 82.11%, whereas the developed scratch model achieved 89.13% classification accuracy.

Tropical cyclone intensity estimation through convolutional neural network transfer learning using two geostationary satellite datasets

Accurate prediction and monitoring of tropical cyclone (TC) intensity are crucial for saving lives, mitigating damages, and improving disaster response measures. In this study, we used a convolutional neural network (CNN) model to estimate TC intensity in the western North Pacific using Geo-KOMPSAT-2A (GK2A) satellite data. Given that the GK2A data cover only the period since 2019, we applied transfer learning to the model using information learned from previous Communication, Ocean, and Meteorological Satellite (COMS) data, which cover a considerably longer period (2011–2019). Transfer learning is a powerful technique that can improve the performance of a model even if the target task is based on a small amount of data. Experiments with various transfer learning methods using the GK2A and COMS data showed that the frozen–fine-tuning method had the best performance due to the high similarity between the two datasets. The test results for 2021 showed that employing transfer learning led to a 20% reduction in the root mean square error (RMSE) compared to models using only GK2A data. For the operational model, which additionally used TC images and intensities from 6 h earlier, transfer learning reduced the RMSE by 5.5%. These results suggest that transfer learning may represent a new breakthrough in geostationary satellite image–based TC intensity estimation, for which continuous long-term data are not always available.

Image Object and Scene Recognition Based on Improved Convolutional Neural Network

In recent years, due to the continuous optimization of network structure and the emergence of large-scale data, Convolutional neural network has made breakthroughs in a series of applications of computer vision. Based on this, the Convolutional neural network is improved and optimized. The improved convolutional neural network is introduced into image Object detection and scene recognition, and image object detection is carried out by combining sliding window fusion and Convolutional neural network. The image scene recognition model is constructed by using potential object area recognition and Convolutional neural network Transfer learning. Using different data sets to verify the algorithm, the research results show that in Group1 and Group2, the error rate of the multi column convolutional neural network fused by sliding window is reduced by about 25% compared with the single column convolutional neural network. As the group with the smallest decrease in error rate, Group3 also achieved a 9% decrease in error rate. The fitness rate of object detection algorithm is gradually stable after 7 runs, reaching about 9.8%, and its operation effect is obviously better than other algorithms. The multi column convolutional neural network fused by sliding window is more adaptive to the training data set, and gets better recognition effect in the algorithm operation. However, the image scene recognition model based on potential object area recognition algorithm and Convolutional neural network has good convergence. The average recognition time for image scenes is 1.5356s. The recognition speed is fast and stable, which can effectively solve the problem of multi-scale image scene recognition.

Vineyard vigilance: Harnessing deep learning for grapevine disease detection

According to the International Organization of Vine and Wine, an impressive 77.8 million tons of grapes are cultivated globally each year. Grapes, long a cornerstone of both diets and agriculture worldwide, are increasingly threatened by diseases such as black rot, Esca, and leaf blight. In modern grapevine orchards, the prevalent method of disease detection relies on human observation, a process that often leads to delays in identifying these afflictions. This inefficiency typically results in diminished crop yields and inferior fruit quality. Automating grapevine leaf disease detection using machine learning will lead to sustainable farming. Deep learning, a branch of machine learning, is well-suited for learning from image data. In this paper, we deployed Convolutional Neural Networks (CNN), a type of deep learning model, to accurately classify healthy and disease-affected grapevine leaf images. We developed a baseline CNN model along with several transfer learning CNN models, namely DenseNet121, EfficientNetB7, MobileNetV2, ResNet50, and VGG16. We hypothesized that ResNet50 would be the best-performing model as it allows deep networks to train with high accuracy, but EfficientNetB7 turned out to be the most accurate model after experimentation. We created a max-voting ensemble with the three most accurate CNN models from the set, and it delivered better results than any individual CNN model. Our max-voting ensemble was then deployed on the web. Consequently, grapevine farmers and other users can detect the three common grapevine leaf diseases – black rot, Esca, and leaf blight, as well as healthy leaves by uploading their own images from the orchard.

Automatic classification of 10 blood cell subtypes using transfer learning via pre-trained convolutional neural networks

Human blood is primarily composed of plasma, red blood cells, white blood cells, and platelets. It plays a vital role in transporting oxygen and nutrients to all organs, and stores essential health-related data about the human body. Blood cells are utilized to defend the body against infections and disease. Hence, analysis of blood permits physicians to assess an individual's physiological condition. Blood cells are sub-classified into eight groups: Neutrophils, eosinophils, basophils, lymphocytes, monocytes, immature granulocytes (promyelocytes, myelocytes, and metamyelocytes), erythroblasts, and platelets (thrombocytes) on the basis of their nucleus, shape and cytoplasm. Traditionally, pathologists and hematologists have identified and examined these via microscopy prior to manual classification, with this manual approach being slow and prone to human error. Therefore, it is essential to automate this process. In the current study, transfer learning with a series of pre-trained Convolutional Neural Network (CNN) models—VGG16, VGG19, ResNet-50, ResNet-101, ResNet-152, InceptionV3, MobileNetV2 and DenseNet-201 was applied to the normal peripheral blood cells dataset (PBC). The overall accuracy achieved with individual CNNs ranged from 91.4 % to 94.7 %. Based on these pre-trained architectures, a CNN-based architecture has been developed to automatically classify all ten blood cell types. The proposed transfer learning CNN model was tested on blood cell images from the PBC, Kaggle and LISC datasets. Achieved accuracy was 99.91 %, 99.68 % and 98.79 %, respectively, across these three datasets. The presented CNN architecture outperforms all previous results reported in the scientific/medical literature with a high capacity for framework generalization in future applications of blood cell classification.

EMG-based Multi-User Hand Gesture Classification via Unsupervised Transfer Learning Using Unknown Calibration Gestures.

The poor generalization performance and heavy training burden of the gesture classification model contribute as two main barriers that hinder the commercialization of sEMG-based human-machine interaction (HMI) systems. To overcome these challenges, eight unsupervised transfer learning (TL) algorithms developed on the basis of convolutional neural networks (CNNs) were explored and compared on a dataset consisting of 10 gestures from 35 subjects. The highest classification accuracy obtained by CORrelation Alignment (CORAL) reaches more than 90%, which is 10% higher than the methods without using TL. In addition, the proposed model outperforms 4 common traditional classifiers (KNN, LDA, SVM, and Random Forest) using the minimal calibration data (two repeated trials for each gesture). The results also demonstrate the model has a great transfer robustness/flexibility for cross-gesture and cross-day scenarios, with an accuracy of 87.94% achieved using calibration gestures that are different with model training, and an accuracy of 84.26% achieved using calibration data collected on a different day, respectively. As the outcomes confirm, the proposed CNN TL method provides a practical solution for freeing new users from the complicated acquisition paradigm in the calibration process before using sEMG-based HMI systems.

Acoustic emission reflection signal classification of PVDF-type AE sensor using convolutional neural network-transfer learning

Acoustic emission reflection signal classification of PVDF-type AE sensor using convolutional neural network-transfer learning

Deep transfer learning CNN based for classification quality of organic vegetables

This study introduces a system based on a Convolutional Neural Network (CNN) with deep transfer learning for classifying organic vegetables. It aims to evaluate their quality through artificial intelligence. The approach involves three key steps: collecting data, preparing data, and creating data models. Initially, the data collection phase involves gathering images of organic vegetables from packing facilities, organizing these images into training, testing, and validation datasets. In the preparation phase, image processing techniques are applied to adjust the images for training and testing, resizing each to 224 x 224 pixels. The modeling phase involves using these prepared datasets, which include 3,239 images of two types of organic vegetables, to train the model. The study tests the model's effectiveness using three CNN architectures: Inception V3, VGG16, and ResNet50. It finds that the Inception V3 model achieves the highest accuracy at 85%, VGG16 follows with 82% accuracy, and ResNet50 has the lowest accuracy at 50%. The results suggest that Inception V3 is the most effective at accurately classifying organic vegetables, while VGG16 shows some limitations in certain categories, and ResNet50 is the least effective.

Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks.

Anemia is defined as a drop in the number of erythrocytes or hemoglobin concentration below normal levels in healthy people. The increase in paleness of the skin might vary based on the color of the skin, although there is currently no quantifiable measurement. The pallor of the skin is best visible in locations where the cuticle is thin, such as the interior of the mouth, lips, or conjunctiva. This work focuses on anemia-related pallors and their relationship to blood count values and artificial intelligence. In this study, a deep learning approach using transfer learning and Convolutional Neural Networks (CNN) was implemented in which VGG16, Xception, MobileNet, and ResNet50 architectures, were pre-trained to predict anemia using lip mucous images. A total of 138 volunteers (100 women and 38 men) participated in the work to develop the dataset that contains two image classes: healthy and anemic. Image processing was first performed on a single frame with only the mouth area visible, data argumentation was preformed, and then CNN models were applied to classify the dataset lip images. Statistical metrics were employed to discriminate the performance of the models in terms of Accuracy, Precision, Recal, and F1 Score. Among the CNN algorithms used, Xception was found to categorize the lip images with 99.28% accuracy, providing the best results. The other CNN architectures had accuracies of 96.38% for MobileNet, 95.65% for ResNet %, and 92.39% for VGG16. Our findings show that anemia may be diagnosed using deep learning approaches from a single lip image. This data set will be enhanced in the future to allow for real-time classification.

A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images

Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. In this article, it is the first time that satellite remote sensing (RS) data is utilized to monitor the shipyard production state dynamically and efficiently, which can make up for the traditional production state data collection mode. According to the imaging characteristics of optical remote sensing images in shipyards with a different production state, the characteristics are analyzed to establish reliable production state evidence. Firstly, in order to obtain the characteristics of the production state of optical remote sensing data, the high-level semantic information in the shipyard is extracted by transfer learning convolutional neural networks (CNNs). Secondly, in the evidence fusion, for the conflict evidence from the core sites of the shipyard, an improved DS evidence fusion method is proposed, which constructs the correlation metric to measure the degree of conflict in evidence and designs the similarity metric to measure the credibility of evidence. Thirdly, the weight of all the evidence is calculated according to the similarity metric to correct the conflict evidence. The introduction of the iterative idea is motivated by the fact that the fusion result aligns more closely with the desired result, the iterative idea is introduced to correct the fusion result. This method can effectively solve the conflict of evidence and effectively improve the monitoring accuracy of the shipyard production state. In the experiments, the Yangtze River Delta and the Bohai Rim are selected to verify that the proposed method can accurately recognize the shipyard production state, which reveals the potential of satellite RS images in shipyard production state monitoring, and also provides a new research thought perspective for other industrial production state monitoring.