Defect detection and classification of galvanized stamping parts based on fully convolution neural network

Convolutional Neural Network (CNN) is a deep learning algorithm that is widely used to identify and classify a digital image object. In this study the Convolutional Neural Network (CNN) is used as an algorithm that functions to identify leaf types (certain plants) based on images obtained from a public dataset provider named Daun Jamu Indonesia. The existence of image characteristics causes the assistance process to require a more detailed feature selection process. Therefore the CNN method is used in order to solve the problem. The Convolutional Neural Network (CNN) method is capable of performing image recognition by minimizing feature extraction. CNN is also reliable in processing unstructured data because it uses a multi-layered structure of artificial reasoning networks. The image recognition process is carried out by looking for the shape of the model that matches the processed data in order to get the best results. In this study, the augmentation process was carried out on the training data and validation data so that overfitting does not occur in the Convolutional Neural Network (CNN). The results obtained in this study indicate that the Convolutional Neural Network (CNN) method can identify leaf types with a measured accuracy rate of 92% using the Confusion Matrix evaluation method. It is hoped that this research can be used as a reference for the use of the Convolutional Neural Network (CNN) method for image data, especially plant leaf types.

Unmanned Aerial Vehicles (UAVs) have revolutionized data acquisition across various domains, presenting immense potential for image processing and semantic segmentation. This literature review encompasses a thorough exploration of advancements, techniques, challenges, and datasets pertaining to UAV image semantic segmentation. It begins by introducing the fundamental concepts of UAVs, highlighting their pivotal role in capturing high-resolution imagery that serves diverse applications. The integration of deep learning algorithms with UAVs is emphasized, unlocking new horizons in autonomous flight, security, and environmental monitoring. Delving into the core principles of semantic segmentation, the review elucidates the critical task of classifying every pixel in an image. Convolutional Neural Networks (CNNs) are presented as the cornerstone technology, tracing their evolution from traditional CNNs to the highly adaptable Fully Convolutional Networks (FCNs). A substantial portion of the review is dedicated to FCNs, underscoring their ability to process images of varying dimensions while maintaining spatial coherence in the output. Their pivotal role in semantic segmentation, encompassing both classification and localization, is articulated. The subsequent sections delve into a comprehensive survey of state-of-the-art models, including SegNet, PSPNet, DeepLabNet, EfficientNet, DenseNet-C, and LinkNet. Each model's unique strengths and applications contribute to the evolving landscape of semantic segmentation tasks. The versatility of the U-Net architecture takes center stage in the latter parts of the review. Its fundamental structure is elucidated, followed by a comprehensive examination of its manifold adaptations—3D-U-Net, ResU-Net, U-Net++, Adversarial U-Net, Cascaded U-Net, and Improved U-Net 3+. These modifications address intrinsic challenges such as limited receptive fields and class imbalances, propelling U-Net to the forefront of image segmentation techniques. The subsequent sections pivot toward the application of U-Net in UAV image segmentation, illustrating its efficacy in diverse tasks, including land cover and crop classification. Nevertheless, persisting challenges, such as the scarcity of annotated datasets and the need for model generalization across varied environmental conditions, remain key areas of concern. The review culminates by underlining the significance of large, authentic datasets and data augmentation techniques. Furthermore, a brief exploration of publicly available UAV image datasets is presented, enhancing our understanding of the resources accessible for training and evaluating models. This comprehensive literature review encapsulates the dynamism of UAV image processing and semantic segmentation, illuminating recent developments and avenues for future research in this burgeoning field.

This research aims to implement deep learning techniques to determine fact and fake news in Indonesian language. The methods used are Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The stages of the research consisted of collecting data, labeling data, preprocessing data, word embedding, splitting data, forming CNN and LSTM models, evaluating, testing new input data and comparing evaluations of the established CNN and LSTM models. The Data are collected from a fact and fake news provider site that is valid, namely TurnbackHoax.id. There are 1786 news used in this study, with 802 fact and 984 fake news. The results indicate that the CNN and LSTM methods were successfully applied to determine fact and fake news in Indonesian language properly. CNN has an accuracy test, precision and recall value of 0.88, while the LSTM model has an accuracy test and precision value of 0.84 and a recall of 0.83. In testing the new data input, all of the predictions obtained by CNN are correct, while the prediction results obtained by LSTM have 1 wrong prediction. Based on the evaluation results and the results of testing the new data input, the model produced by the CNN method is better than the model produced by the LSTM method.

Image semantic segmentation contains two sub-tasks, segmenting and labeling. However, the recent fully convolutional network (FCN) based methods often ignore the first sub-task and consider it as a direct labeling one. Even though these methods have achieved competitive performances, they obtained spatially fragmented and disconnected outputs. The reason is that, pixel-level relationships inside the deepest layers become inconsistent since traditional FCNs do not have any explicit pixel grouping mechanism. To address this problem, a multi-step supervised learning method, which contains image-level supervised learning step and pixel-level supervised learning step, is proposed. Specifically, as for the visualized result of image semantic segmentation, it is actually an image-to-image transformation problem, from RGB domain to category label domain. The recent conditional generative adversarial network (cGAN) has achieved significant performance for image-to-image generation task, and the generated image remains good regional connectivity. Therefore, a cGAN supervised by RGB-category label map is used to obtain a coarse segmentation mask, which avoids generating disconnected segmentation results to a certain extent. Furthermore, an interaction information (II) loss term is proposed for cGAN to remain the spatial structure of the segmentation mask. Additionally, dilated convolutional networks (DCNs) have achieved significant performance in object detection field, especially for small objects because of its special receptive field settings. Specific to image semantic segmentation, if each pixel is seen as an object, this task can be transformed to object detection. In this case, combined with the segmentation mask from cGAN, a DCN supervised by the pixel-level label is used to finalize the category recognition of each pixel in the image. The proposed method achieves satisfactory performances on three public and challenging datasets for image semantic segmentation.

To facilitate the management of detonators, an automatic spotting system is proposed for detonator coded characters based on convolutional neural networks. The system contains a multi-scale detection network, a multi-label recognition network and a post-processing layer. An improved fully convolution network (FCN) is designed as the multi-scale detection network to obtain the accurate response map of the detonator image. Two subnetworks are parallel integrated into the FCN to perform a coarse-to-fine detection. An improved Jaccard loss function with a regularization term is defined to train the FCN. The region of interest (ROI) of the detonator image is achieved when the response map is post-processed by the post-processing layer. Finally, a modified multi-label network is used to recognize the detonator coded characters in the ROI. The experimental results indicate that the proposed system achieves a better spotting performance for detonator coded characters than the state-of-the-art text spotting methods in terms of accuracy and efficiency.

Brain Computer Interface (BCI) is a system that connects the human brain with the outside world for people who have motor skills disability problems. One form of utilization is the P300 speller which is used for character recognition or detection by classifying the P300 signal. The Convolutional Neural Network (CNN) method is a deep learning method that can be used to handle signal problems with ID-CNN. At the initial stage the data signal will be transformed and followed by a duplication process using RandomOverSampling because the amount of data in each class is not balanced. The data will be divided into training, validation, and test data. After that, a training with CNN will be conducted and followed by an evaluation to find the best model. The test results from this study are a good-fitting CNN model with an evaluation value consisting of an accuracy of 94.27%, precision of 90.64%, sensitivity / recall of 98.30%, and f-measure of 94.31%. Based on the test, the CNN method can be used and implemented in authentication detection based on the P300 signal.

Aiming at the time-consuming problem of the full-wave (FW) simulation of the scattering characteristics of the traditional graphene reconfigurable reflectarray antenna, a fast prediction method of electromagnetic (EM) response based on deep learning is proposed. The convolutional neural network (CNN) method in deep learning is effectively used in the research of this paper. This method first discretizes the input vector (patch geometry, chemical potential, frequency, incident angle, etc.) of the graphene reflectarray antenna, and then preprocesses the data into a two-dimensional image suitable for CNN training, and finally uses CNN to train the model instead of extensive FW simulation calculations, the EM response of the reflectarray antenna is calculated. The training results of three algorithms of support vector regression (SVR), radial basis function network (RBFN) and CNN are comprehensively compared. The experimental results show that CNN method has good performance and accuracy in the EM response prediction of the graphene reconfigurable reflectarray antenna, with an accuracy of over 99%, and can also save at least 99% of time.

Some researchers propose using the Convolutional Neural Network (CNN) method to classified batik images. It can extract features automatically without the need to define feature manually from the image. However, CNN’s weakness is that its accuracy is quite low, especially for small-sized datasets, compared to machine learning methods that use handcrafted feature extraction. In this research, an ensemble CNN method is proposed to improve the accuracy of the CNN method in classifying batik images. This method will train several CNN models at once, and then by voting and averaging techniques, the output label will be determined. Test results for two different datasets show this method can improve the accuracy of the CNN method and get an accuracy value of 100%. This method is also proven to extract features faster than the state-of-the-art method like MTCD+SVM, which is included in the hand-crafted feature extraction category.

Garbage is a unique problem in Indonesia. From ordinary waste to limited emergency plastic waste, Indonesia is the second-largest source of plastic waste in the world. Separate collection and disposal of waste is one way to reduce the waste generated by society and industry in Indonesia. Sorting out the types of waste is the first step before the recycling process. In the field of Computer Vision research, it is difficult to see the type and form of waste with a camera, therefore this study aims to overcome this problem by using Deep Learning technology which is expected to be implemented in the whole of Indonesia starting from some of the largest waste-producing cities. Deep Learning is a computer (AI) technique for learning like a human - with experiments being a Part of Machine Learning that can be used to classify images. The method used in this study uses the Convolutional Neural Network (CNN) method which can be used to detect and recognize objects in an image, which can be used to create an automatic waste classification system. Broadly speaking, CNN utilizes the convolution process by moving a convolution kernel (filter) of a certain size to an image, the computer gets new representative information from the results of multiplying that part of the image with the filter used. The test results show that the CNN method can classify inorganic waste with accuracy. 96% and organic waste with an accuracy of 62%.

Abstract: This research aims to develop a method for detecting leaf spot disease in oil palm seedlings using Convolutional Neural Network (CNN). Leaf spot disease in oil palm seedlings can hinder growth and production. CNN has proven effective in image processing and classification, particularly in plant disease detection. In this study, we utilized a dataset of images containing oil palm seedling leaves infected with leaf spot disease and healthy leaves. We performed data processing, built a CNN model, and conducted hyperparameter tuning. The test results demonstrate that the developed CNN model achieves high accuracy in recognizing and distinguishing between oil palm seedling leaves infected with leaf spot disease and healthy ones. This research contributes to the development of plant disease detection technology that can support economic growth in the oil palm plantation sector. Keywords: Convolutional Neural Network, image processing, leaf spot disease detection, oil palm seedlings. Abstrak: Penelitian ini bertujuan untuk mengembangkan metode deteksi penyakit bercak pada bibit kelapa sawit menggunakan Convolutional Neural Network (CNN). Bibit kelapa sawit yang terinfeksi penyakit bercak dapat menghambat pertumbuhan dan produksi kelapa sawit. Metode CNN telah terbukti efektif dalam pengolahan citra dan klasifikasi, khususnya dalam deteksi penyakit pada tanaman. Dalam penelitian ini, kami menggunakan dataset citra daun bibit kelapa sawit yang terinfeksi penyakit bercak dan yang normal. Kami melakukan processing data, membangun model CNN, dan melakukan tuning hyperparameter. Hasil pengujian menunjukkan bahwa model CNN yang dikembangkan memiliki akurasi yang tinggi dalam mengenali dan membedakan citra daun bibit kelapa sawit yang terinfeksi penyakit bercak dan yang normal. Penelitian ini memberikan kontribusi dalam pengembangan teknologi deteksi penyakit tanaman yang dapat mendukung pertumbuhan ekonomi di sektor perkebunan kelapa sawit. Kata kunci: bibit kelapa sawit, Convolutional Neural Network, deteksi penyakit bercak, pengolahan citra.

Skin disease is the most common disease and the fastest to infect the human body. This happens because the skin is the first organ to receive external stimuli in the form of touch, temperature and other stimuli. Skin diseases consist of several types that have almost the same color and texture with the naked eye. Thus, an approach is needed to identify the type of skin disease with the help of an image processing system, and an artificial neural network. The identification method used in this research is Convolutional Neural Network (CNN). The infected skin image is used as an input image for image processing. Before being identified, image preprocessing is done, namely resizing, grayscalling, using the Convolutional Neural Network method. The testing process in this study uses 70 types of skin disease images, validation data and 35 types of skin disease images for testing data. The results of this study are the Convolutional Neural Network method can recognize each type of skin disease image with an accuracy of 98% in the validation testing process and 85% in the testing process.
 Keywords : Skin disease, Convolutional Neural Network, Digital Image Processing

Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: A semantic segmentation solution

One of the conditions for hiring workers in Indonesian companies is to pass a psychology test. Psychology test has many types of tests, one of which is the Baum test, a tree-drawing test. Baum test requires participants to draw a tree following the imagination of the participants. Later, the picture of the participant tree obtained from the Baum test will be assessed by psychologists as one of the labor assessment tools. This assessment is based on the shape of the tree crown, leaf shape, tree size on paper, etc. Currently, the assessment of the Baum test is still done manually by a psychologist. In order to help psychologists in assessing the Baum test, we propose an application in the form of a model that can study and classify tree images based on the tree size in the image by using the Convolutional Neural Network (CNN) method. CNN method allows computers to classify an object. The data are provided in the form of digital images of tree images from Baum testing that has been conducted. The data that has been collected will be used to train the model. The model used in this study has an architecture in the form of a convolution layer with a kernel size of 3×3, a pooling layer with a parameter size of 2×2, a dropout layer, a flatten layer, and a dense layer. The model was trained with 60 epochs and had a test and training data scale of 70:30. The model in this research has an accuracy of 74.07% in predetermined data testing and has an accuracy of 75% in new data testing.

Hybrid CNN-transformer network for interactive learning of challenging musculoskeletal images

Semantic segmentation with deep learning has achieved great progress in classifying the pixels in the image. However, the local location information is usually ignored in the high-level feature extraction by the deep learning, which is important for image semantic segmentation. To avoid this problem, we propose a graph model initialized by a fully convolutional network (FCN) named Graph-FCN for image semantic segmentation. Firstly, the image grid data is extended to graph structure data by a convolutional network, which transforms the semantic segmentation problem into a graph node classification problem. Then we apply graph convolutional network to solve this graph node classification problem. As far as we know, it is the first time that we apply the graph convolutional network in image semantic segmentation. Our method achieves competitive performance in mean intersection over union (mIOU) on the VOC dataset(about 1.34% improvement), compared to the original FCN model.