Rural Building Detection in High-Resolution Imagery Based on a Two-Stage CNN Model

Text recognition in natural scene images is a challenging problem in computer vision. Different than the optical character recognition (OCR), text recognition in natural scene images is more complex due to variations in text size, colors, fonts, orientations, complex backgrounds, occlusion, illuminations and uneven lighting conditions. In this paper, we propose a segmentation-free method based on a deep convolutional recurrent neural network to solve the problem of cursive text recognition, particularly focusing on Urdu text in natural scenes. Compared to the non-cursive scripts, Urdu text recognition is more complex due to variations in the writing styles, several shapes of the same character, connected text, ligature overlapping, stretched, diagonal and condensed text. The proposed model gets a whole word image as an input without pre-segmenting into individual characters, and then transforms into the sequence of the relevant features. Our model is based on three components: a deep convolutional neural network (CNN) with shortcut connections to extract and encode the features, a recurrent neural network (RNN) to decode the convolutional features, and a connectionist temporal classification (CTC) to map the predicted sequences into the target labels. To increase the text recognition accuracy further, we explore deeper CNN architectures like VGG-16, VGG-19, ResNet-18 and ResNet-34 to extract more appropriate Urdu text features, and compare the recognition results. To conduct the experiments, a new large-scale benchmark dataset of cropped Urdu word images in natural scenes is developed. The experimental results show that the proposed deep CRNN network with shortcut connections outperform than other network architectures. The dataset is publicly available and can be downloaded from <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://data.mendeley.com/datasets/k5fz57zd9z/1</uri> .

Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescription of more effective treatments adapted to the specificities of each individual case. In the last years, next-generation sequencing has impelled cancer research by providing physicians with an overwhelming amount of gene-expression data from RNA-seq high-throughput platforms. In this scenario, data mining and machine learning techniques have widely contribute to gene-expression data analysis by supplying computational models to supporting decision-making on real-world data. Nevertheless, existing public gene-expression databases are characterized by the unfavorable imbalance between the huge number of genes (in the order of tenths of thousands) and the small number of samples (in the order of a few hundreds) available. Despite diverse feature selection and extraction strategies have been traditionally applied to surpass derived over-fitting issues, the efficacy of standard machine learning pipelines is far from being satisfactory for the prediction of relevant clinical outcomes like follow-up end-points or patient's survival. Using the public Pan-Cancer dataset, in this study we pre-train convolutional neural network architectures for survival prediction on a subset composed of thousands of gene-expression samples from thirty-one tumor types. The resulting architectures are subsequently fine-tuned to predict lung cancer progression-free interval. The application of convolutional networks to gene-expression data has many limitations, derived from the unstructured nature of these data. In this work we propose a methodology to rearrange RNA-seq data by transforming RNA-seq samples into gene-expression images, from which convolutional networks can extract high-level features. As an additional objective, we investigate whether leveraging the information extracted from other tumor-type samples contributes to the extraction of high-level features that improve lung cancer progression prediction, compared to other machine learning approaches.

ABSTRACTOil tank detection is a challenging task, primarily due to high time-consumption. This paper aims at further investigating this challenge and proposes a new hierarchical approach to detect oil tanks, especially with respect to how false alarm rates are reduced. The proposed approach is divided into four stages: region of interest (ROI) extraction, circular object detection, feature extraction, and classification. The first stage, which is a key component of this approach to reduce false alarm and processing time, is applied by an improved faster region-based convolutional neural network (Faster R-CNN) to extract oil depots. In the second stage, a number of candidate objects of the target are selected from the extracted ROIs by a fast circle detection method. Afterwards, in the third stage, a robust feature extractor based on a combination of the output feature vectors from convolutional neural network (CNN), as a high-level feature extractor, and histogram of oriented gradients (HOG), as a low-level feature extractor, are used for representing features of various targets. Finally, the support vector machine (SVM) is employed for classification. The experimental results confirm that the proposed approach has good prediction accuracy and is able to reduce the false alarm rates.

Identification of crops is an important topic in the agricultural domain. Hyperspectral remote sensing data are very useful for crop feature extraction and classification. Remote sensing data is an unstructured data and Convolutional Neural Network (CNN) can work on unstructured data efficiently. This paper presents an evaluation of CNN for crop classification using the Indian Pines standard dataset obtained from the AVIRIS sensor and the study area dataset obtained from the EO-1hyperion sensor. Optimized CNN has been tuned by training the model on different parameters. It has been compared with two classification algorithms: Deep Neural Network (DNN) and Convolutional Autoencoder. According to the test results, the proposed optimized CNN model provided better results as compared to the other two methods. CNN has given 97 ± 0.58% overall accuracy for the Indian Pines standard dataset and 78 ± 2.43% for our study area dataset.

— In today's era, the challenge of object detection within laboratory spaces is paramount. The need for automated monitoring systems supporting object detection within laboratory settings, particularly for service robots, is crucial for scientific research and safety. Utilizing convolutional artificial neural networks has shown promising capabilities in object recognition. This paper proposes a novel method for detecting and localizing specific objects within laboratory environments using deep learning techniques. Employing a lightweight primary feature extractor allows for the identification of crucial object features without imposing excessive computational demands. Additionally, a depthwise separable convolution module is introduced, designed to capture essential components within multi-level convolutional patches specific to laboratory conditions. To address the challenges encountered within laboratory spaces, a new dataset is proposed, encompassing issues such as lighting variations, blurriness, occlusions, and complex backgrounds. Evaluation results indicate that the proposed model surpasses other lightweight object detection models in laboratory settings, offering high accuracy and efficiency. This model can be efficiently implemented on low-tier devices, ensuring real-time object detection and processing within laboratory environments. Keywords — object detection, service robot, convolutional neural networks, You Only Look Once version 5. Abstrak — Di era sekarang ini, tantangan deteksi objek di dalam ruang laboratorium menjadi sangat penting. Kebutuhan akan sistem pemantauan otomatis yang mendukung deteksi objek di dalam laboratorium, khususnya untuk robot servis, sangat penting untuk penelitian ilmiah dan keselamatan. Memanfaatkan jaringan saraf tiruan convolutional telah menunjukkan kemampuan yang menjanjikan dalam pengenalan objek. Makalah ini mengusulkan metode baru untuk mendeteksi dan melokalisasi objek tertentu di dalam lingkungan laboratorium menggunakan teknik pembelajaran mendalam. Menggunakan ekstraktor fitur utama yang ringan memungkinkan identifikasi fitur objek yang penting tanpa membebani komputasi yang berlebihan. Selain itu, modul konvolusi yang dapat dipisahkan secara mendalam juga diperkenalkan, yang dirancang untuk menangkap komponen penting dalam patch konvolusi multi-level yang spesifik untuk kondisi laboratorium. Untuk mengatasi tantangan yang dihadapi di dalam ruang laboratorium, dataset baru diusulkan, yang mencakup masalah seperti variasi pencahayaan, kekaburan, oklusi, dan latar belakang yang kompleks. Hasil evaluasi menunjukkan bahwa model yang diusulkan melampaui model pendeteksian objek ringan lainnya di lingkungan laboratorium, menawarkan akurasi dan efisiensi yang tinggi. Model ini dapat diimplementasikan secara efisien pada perangkat tingkat rendah, memastikan pendeteksian dan pemrosesan objek secara real-time di lingkungan laboratorium. Kata kunci — deteksi objek, robot layanan, jaringan saraf konvolusi, Anda Hanya Melihat Sekali versi 5.

Content-based image retrieval (CBIR) is one of the most important applications of computer vision. In recent years, there have been many important advances in the development of CBIR systems, especially Convolutional Neural Networks (CNNs) and other deep-learning techniques. On the other hand, current CNN-based CBIR systems suffer from high computational complexity of CNNs. This problem becomes more severe as mobile applications become more and more popular. The current practice is to deploy the entire CBIR systems on the server side while the client side only serves as an image provider. This architecture can increase the computational burden on the server side, which needs to process thousands of requests per second. Moreover, sending images have the potential of personal information leakage. As the need of mobile search expands, concerns about privacy are growing. In this article, we propose a fast image search framework, named DeepSearch, which makes complex image search based on CNNs feasible on mobile phones. To implement the huge computation of CNN models, we present a tensor Block Term Decomposition (BTD) approach as well as a nonlinear response reconstruction method to accelerate the CNNs involving in object detection and feature extraction. The extensive experiments on the ImageNet dataset and Alibaba Large-scale Image Search Challenge dataset show that the proposed accelerating approach BTD can significantly speed up the CNN models and further makes CNN-based image search practical on common smart phones.

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a lesser number of training cases than did CNNs. "Deep learning", or ML with image input, in medical imaging is an explosively growing, promising field. It is expected that ML with image input will be the mainstream area in the field of medical imaging in the next few decades.

Taking rural buildings in Hebei as an example, the reconstruction experiment was carried out. After a survey on the status quo of buildings in rural areas of Hebei province, it is found that the thermal performance of the envelope structure is poor. Then, an experimental model is established by using DeST software, and relevant parameters are calculated to simulate the contribution rate of energy conservation. It is concluded that the envelope material with superior thermal insulation performance is conducive to promoting the development of rural low-carbon buildings in Hebei Province. According to the above conclusions, the simulation experiment of the rural building envelope is carried out, the method of comparison experiment is used to calculate the relevant parameters, and the thermal performance coefficient of the reconstructed envelope structure is compared with that of the traditional building. It is concluded that the reconstructed building is more in line with the local building energy saving standards, with higher energy saving, and more suitable for extensive application in the rural building renovation.

The location and intensity of non-resolved space object can be obtained in space-based surveillance, however, the material, size, and status of the object are lost. Spectrum represents the inherent property difference of the object, which can be used as an important means for feature extraction and recognition of non-resolved space object. According to the influence factors, including material, structure, background and orbit, a mathematical model for spectral properties of space object is established. Based on the model, inverse calculation method for feature extraction and recognition of space object is proposed. Taking the HuanJing-1 satellite scale model as an example, experimental verification for feature extraction and recognition of space object in typical parameters is made. Experimental results demonstrate the validity of the modeling method.

&amp;lt;p&amp;gt;With consumer-grade unmanned aerial vehicles (UAVs) on the rise, which enable easy, time-flexible, and cost-effective acquisition of very high-resolution RGB data, the mapping of forest tree species using solely RGB imagery is of high interest, as it does not rely on sophisticated sensors, does not require extensive calibration and preprocessing and, therefore, enables the application by a wide audience. In combination with convolutional neural networks (CNNs), which particularly exploit spatial patterns and, therefore, highly benfit from very high-resolution remote sensing, this offers great potential for accurately mapping forest tree species.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Here, we present the findings of our recent study, in which we used very high-resolution RGB imagery from UAVs in combination with CNNs for the mapping of forest tree species. In this study, we used multicopter UAVs to obtain very high-resolution (&amp;lt;2 cm) RGB imagery over 51&amp;amp;#160;ha of temperate forests in the Southern Black Forest region, and the Hainich National Park in Germany. To fully harness the end-to-end learning capabilities of CNNs, we used a semantic segmentation approach (U-net) that concurrently segments and classifies tree species from imagery. With a diverse dataset in terms of study areas, site conditions, illumination properties, and phenology, we accurately mapped nine tree species, three genus-level classes, deadwood, and forest floor (mean F1-score 0.73). We found that a coarser spatial resolution substantially reduced the model accuracy (mean F1-score of 0.26 at 32&amp;amp;#160;cm resolution) and that larger tile sizes during CNN training negatively affected the model accuracies for underrepresented classes. Additional height information from normalized digital surface models slightly increased the model accuracy but simultaneously increased computational complexity and data requirements. Our results highlight the key role that UAVs can play in the mapping of forest tree species, given that air- and spaceborne remote sensing currently does not provide comparable spatial resolutions. Given the end-to-end learning capabilites of CNNs extensive preprocessing becomes partly obsolete, whereas the use of a large and diverse dataset facilitates a high degree of generalization of the CNN, thus fostering transferability. The synergy of high-resolution UAV imagery and CNN provide a fast and flexible yet accurate means of mapping forest tree species.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this contribution, we will give an outlook on how the combination of UAV imagery and CNNs can be integrated with multitemporal satellite imagery (Sentinel-1 and Sentinel-2) in order to extrapolate the UAV-based tree species maps to larger areas.&amp;lt;/p&amp;gt;

The December 29, 2020, Petrinja earthquake highlighted the significance of maintenance of buildings in seismically active regions. Many family houses in rural parts of central Croatia were damaged by the Petrinja earthquake. During the earthquake, the insufficient response of the buildings in the rural regions resulted in the majority of human injuries and fatalities. These deaths and catastrophic property losses in rural areas have underlined the importance of earthquake prevention and mitigation in these communities. A study of the seismic performance of structures in rural areas, as well as an evaluation of seismic risk and susceptibility, were conducted with the aim to improve the overall capability to prevent seismic disasters in rural areas. The structural characteristics, construction method, and application of seismic codes and building rules of two rural villages in the Osijek-Baranja County were researched, and they can be considered representative of the continental area of Croatia and surrounding countries. GIS technology is used to process and present information on the location, distribution, and other characteristics of structures.KeywordsSeismic vulnerabilityBuilding exposure modelRural areasGeo-graphic information system

paper, we focus on exploring the relationship between weave patterns and their mechanical properties in woven fiber composites through Machine Learning. Specifically, we explore the interactions between woven architectures and in-plane stiffness properties through Deep Convolutional Neural Network (DCNN) and Generative Adversarial Network (GAN). Our research is important for exploring how woven composite’s pattern is related to its mechanical properties and accelerating woven composite design as well as optimization. We focus on two tasks: (1) Stiffness prediction: Predicting in-plane stiffness properties for given weave patterns. Our DCNN extracts high-level features through several convolutional and fully connected layers to determine the final predictions. (2) Weave pattern prediction: Predicting weave patterns for target stiffness properties, which can be treated as the reverse task of the first one. Due to many-to-one mapping between weave patterns and the composite properties, we utilize a Decoder Neural Network as our baseline model and compare its performance with GAN and Genetic Algorithm. We represent the weave patterns as 2D checkerboard models and use finite element analysis (FEA) to determine in-plane stiffness properties, which serve as input data for our ML framework. We show that: (1) for stiffness prediction, DCNN can predict stiffness values for a given weave pattern with relatively high accuracy (above 93%); (2) for weave pattern prediction, the GAN model gives the best prediction accuracy (above 92%) while Decoder Neural Network has the best time efficiency. HAOTIAN FENG

Real-world License plate detections presents notable challenges arising due to some factors such as variable illumination, occlusions, complex backgrounds and plate distortions. Traditional approaches such as edge detection often generate numerous false positives under such conditions, while template matching methods typically struggle with adaptability to diverse plate styles. Although Convolutional Neural Networks (CNNs) have improved feature extraction capabilities, it may still misallocate attention to irrelevant regions, reducing detection accuracy. To overcome these limitations, this study proposes an refined deep learning architecture that integrates Convolutional Block Attention Modules (CBAM) into CNN-based feature extractors. By jointly exploiting both channel and spatial attention mechanisms, the model selectively highlights salient features and minimizes the influence of background noise, enhancing its focus on license plate regions across varied image environment. Experimental evaluation using Kaggle License Plate Dataset shows that the proposed CNN + CBAM framework achieves a precision of 92.41%, recall of 89.73%, F1-score of 91.05%, and mAP@0.5 of 88.9%. These results represents an improvement of 7.82% in F1-score and 10.25% in mAP compared to the baseline CNN, validating that the extracted crucial features substantially improve detection accuracy while minimizing false detections. The proposed approach also demonstrates a significantly reduced false detection rate of 5.6%, outperforming both traditional techniques and conventional CNNs based models.

HighlightsWhat are the main findings?Proposed an innovative two-channel hybrid model by integrating convolutional neural networks (CNNs) with a text-based classifier.Leveraged an extended dataset derived from multiple object recognition models, increasing input data diversity and achieving a text-based classifier accuracy of 73.30%. Achieved a significant improvement of 8.33% in accuracy compared to CNN-only models, with the hybrid model attaining an accuracy of 90.46%.What is the implication of the main finding?Efficient and Scalable Methodology: Utilized EfficientNet for CNN-based feature extraction and Bag-of-Words for text representation, ensuring computational efficiency and scalability.Application Potential: Addressed challenges in indoor scene recognition, such as complex backgrounds and object diversity, demonstrating significant potential for applications in robotics, intelligent surveillance, and assistive systems.Indoor scene recognition is a computer vision task that identifies various indoor environments, such as offices, libraries, kitchens, and restaurants. This research area is particularly significant for applications in robotics, security, and assistance for individuals with disabilities, as it enables the categorization of spaces and the provision of contextual information. Convolutional Neural Networks (CNNs) are commonly employed in this field. While CNNs perform well in outdoor scene recognition by focusing on global features such as mountains and skies, they often struggle with indoor scenes, where local features like furniture and objects are more critical. In this study, the “MIT 67 Indoor Scene” dataset is used to extract and combine features from both a CNN and a text-based model utilizing object recognition outputs, resulting in a two-channel hybrid model. The experimental results demonstrate that this hybrid approach, which integrates natural language processing and image processing techniques, improves the test accuracy of the image processing model by 8.3%, achieving a notable success rate. Furthermore, this study offers contributions to new application areas in remote sensing, particularly in indoor scene understanding and indoor mapping.

It is challenging to detect emotions on user-generated contents (UGC) because it tends to have sparse emotional semantics, multiple emotions in the same text, and a fast update of emotional expression. Word embedding can extract high-level features in words to enrich their semantics. The convolutional neural networks (CNNs) can make model training more efficient to adapt to fast-changing emotional expressions, but its original pooling operation can not simultaneously filter out multiple emotional features that are beneficial to classification results, which is not suitable for UGC. In this paper, we propose a self-attentive convolutional neural networks (SACNNs) trained on top of pre-trained word vectors for emotion detection on UGC, which reserves different kinds of emotion information, avoids the loss of emotional aspects in the pooling process of CNNs structure, and increases the interpretability of the model by visualizing the extraction process of features. Meanwhile, the convergence speed of the model training is accelerated, and the model is updated in real time to detect emotions with rich and novel expressions. The proposed model combines the CNNs and the self-attention mechanism, the self-attention mechanism can select key emotional features after convolution. We evaluate the proposed model with two datasets from NLPCC 2014 and SemEval 2018 Task 1. The experimental results show that our model obtain significant performance than baselines in multi-label classification on UGC. In addition, the experimental results and the rationality of the self-attention mechanism are analyzed in detail, and the influential convolutional filter windows are visualized based on attention weights.