2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)最新文献_第10页

Detection and Identification of Strip Surface Defects Based on Deep Learning 基于深度学习的带钢表面缺陷检测与识别

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00086

Y. Zhan, Feng Feng

{"title":"Detection and Identification of Strip Surface Defects Based on Deep Learning","authors":"Y. Zhan, Feng Feng","doi":"10.1109/MLISE57402.2022.00086","DOIUrl":"https://doi.org/10.1109/MLISE57402.2022.00086","url":null,"abstract":"As one of the important product forms in the steel industry, strip steel plays an irreplaceable role in daily production and life. However, in real life, due to the use of raw materials and the manufacturing process, there are many defects on the surface of strip steel. These defects will not only affect the appearance of strip steel, but also cause significant economic losses. Therefore, it is particularly important to accurately detect these defects on the surface of strip steel. Aiming at the problem that there are many tiny defects in strip steel, which can easily lead to missed detection, this paper improves it based on Faster R-CNN detection algorithm, uses ResNet50+FPN method to extract features to prevent missing tiny defect details, and uses ROI Align instead of ROI Pooling to perform pool operation of defect location. Because the position deviation caused by the two quantization operations of ROI Pooling can prevent the slight defect of missing detection, the experimental verification finally shows that the improved measures effectively improve the accuracy of strip surface detection compared with the previous detection algorithm.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124502288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Study of Transferability of ImageNet-Based Pretrained Model to Brain Tumor MRI Dataset 基于imagenet的预训练模型到脑肿瘤MRI数据集的可移植性研究

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00025

Zhiyuan Chen

{"title":"Study of Transferability of ImageNet-Based Pretrained Model to Brain Tumor MRI Dataset","authors":"Zhiyuan Chen","doi":"10.1109/MLISE57402.2022.00025","DOIUrl":"https://doi.org/10.1109/MLISE57402.2022.00025","url":null,"abstract":"Brain tumor detection is an active research problem in the field of computer-aided diagnosis in the medical field. While many works used convolutional neural networks (CNN) with transfer learning and addressed this problem with great performance, the interpretability of these transfer learning models was still unclear. In this paper, four different transfer learning settings were tested over three CNN structures including MobileNet, EfficientNet, and ResNet. The first setting is to use a model without transfer learning, the second setting is to use transfer learning keeping all layers learnable; the third setting is to use transfer learning with the first 1/3 layers frozen; the last setting is to use transfer learning with first 2/3 layers frozen. All the pre-trained models were trained on the ImageNet dataset. For each CNN structure and each transfer learning setting, a model was created and trained on the brain Magnetic Resonance Imaging (MRI) dataset. After all the 12 models had been trained, their performance and learned features were compared. Experimental results indicate that the setting with 1/3 layers frozen outperforms other settings, showing the transferability of the first 1/3 layers of models trained on ImageNet to the brain MRI dataset.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129466741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

American Sign Language Recognition Based on Machine Learning and Neural Network 基于机器学习和神经网络的美国手语识别

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/mlise57402.2022.00096

Lanxi Li, Da Liu, Chenlin Shen, Jing Sun

{"title":"American Sign Language Recognition Based on Machine Learning and Neural Network","authors":"Lanxi Li, Da Liu, Chenlin Shen, Jing Sun","doi":"10.1109/mlise57402.2022.00096","DOIUrl":"https://doi.org/10.1109/mlise57402.2022.00096","url":null,"abstract":"Numerous disabilities such as deaf and mute are suffered from not being capable of communicating with normal people, it is necessary to find a way to solve this problem. A feasible method is Sign Language Recognition (SLR) which is a sort of pattern recognition technique. In this paper, machine learning and deep learning methods are applied to recognize and classify American Sign Language (ASL), and only 24 English letters are classified because letter J and Z require fingers to move. First, Principal Component Analysis (PCA) and manifold algorithms are used to do dimension reduction to accelerate the training of machine learning and visualize it. Second, various machine learning methods such as Random Forest Classification (RFC), K-Nearest Neighbor (KNN), Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD) are applied to classify the pattern. Since the SVM algorithm has several hyperparameters, this study uses the Grid Search method to find the best combination of hyperparameter which lead to predicting more accurately. It is found that different dimensionality reduction algorithms have unequal effects on the accuracy of each prediction model, and it can be concluded that the manifold algorithm is the best dimension reduction algorithm only for KNN but not for other prediction models, and PCA is much more feasible than KNN applied in such machine learning algorithms except KNN. Two deep learning algorithms such as Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) are also used in classification and their accuracy is highest among such algorithms mentioned above.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129542748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Design of GPS Service System for Vehicle Monitoring of Intelligent Expressway 智能高速公路车辆监控GPS服务系统设计

2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2022-08-01 DOI: 10.1109/MLISE57402.2022.00047

Changhua Wang, Hancheng Yu

{"title":"Design of GPS Service System for Vehicle Monitoring of Intelligent Expressway","authors":"Changhua Wang, Hancheng Yu","doi":"10.1109/MLISE57402.2022.00047","DOIUrl":"https://doi.org/10.1109/MLISE57402.2022.00047","url":null,"abstract":"with the rapid development of Expressway in China, the problems of environmental pollution, traffic congestion and frequent accidents caused by expressway have become increasingly prominent. It is necessary to establish a perfect, advanced and efficient expressway monitoring system to manage expressway. The Internet of things (IOT) is a new technology emerging in recent years. It integrates information technology, electronic control technology, sensor technology and other advanced technologies to monitor and manage objects, and realizes the information transmission between objects and people. Applying the Internet of things technology to the monitoring and management of expressway can improve the operation efficiency of Expressway and reduce the occurrence of Expressway accidents. This paper studies the expressway monitoring system based on Internet of things. The research on Expressway Monitoring System Based on Internet of things technology is in line with the current national development strategy of vigorously developing Internet of things technology, and lays a theoretical foundation for the further application of Internet of things Expressway Monitoring System in the future.","PeriodicalId":350291,"journal":{"name":"2022 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115477038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0