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Fire incidents near power transmission lines pose significant safety hazards to the regular operation of the power system. Therefore, achieving fast and accurate smoke detection around power transmission lines is crucial. Due to the complexity and variability of smoke scenarios, existing smoke detection models suffer from low detection accuracy and slow detection speed. This paper proposes an improved model for smoke detection in high-voltage power transmission lines based on the improved YOLOv7-tiny. First, we construct a dataset for smoke detection in high-voltage power transmission lines. Due to the limited number of real samples, we employ a particle system to randomly generate smoke and composite it into randomly selected real scenes, effectively expanding the dataset with high quality. Next, we introduce multiple parameter-free attention modules into the YOLOv7-tiny model and replace regular convolutions in the Neck of the model with Spd-Conv (Space-to-depth Conv) to improve detection accuracy and speed. Finally, we utilize the synthesized smoke dataset as the source domain for model transfer learning. We pre-train the improved model and fine-tune it on a dataset consisting of real scenarios. Experimental results demonstrate that the proposed improved YOLOv7-tiny model achieves a 2.61% increase in mean Average Precision (mAP) for smoke detection on power transmission lines compared to the original model. The precision is improved by 2.26%, and the recall is improved by 7.25%. Compared to other object detection models, the smoke detection proposed in this paper achieves high detection accuracy and speed. Our model also improved detection accuracy on the already publicly available wildfire smoke dataset Figlib (Fire Ignition Library). [ABSTRACT FROM AUTHOR]

In recent years, deep learning has been widely used in breast cancer diagnosis, and many high-performance models have emerged. However, most of the existing deep learning models are mainly based on static breast ultrasound (US) images. In actual diagnostic process, contrast-enhanced ultrasound (CEUS) is a commonly used technique by radiologists. Compared with static breast US images, CEUS videos can provide more detailed blood supply information of tumors, and therefore can help radiologists make a more accurate diagnosis. In this paper, we propose a novel diagnosis model based on CEUS videos. The backbone of the model is a 3D convolutional neural network. More specifically, we notice that radiologists generally follow two specific patterns when browsing CEUS videos. One pattern is that they focus on specific time slots, and the other is that they pay attention to the differences between the CEUS frames and the corresponding US images. To incorporate these two patterns into our deep learning model, we design a domain-knowledge-guided temporal attention module and a channel attention module. We validate our model on our Breast-CEUS dataset composed of 221 cases. The result shows that our model can achieve a sensitivity of 97.2% and an accuracy of 86.3%. In particular, the incorporation of domain knowledge leads to a 3.5% improvement in sensitivity and a 6.0% improvement in specificity. Finally, we also prove the validity of two domain knowledge modules in the 3D convolutional neural network (C3D) and the 3D ResNet (R3D). [ABSTRACT FROM AUTHOR]

Diabetic kidney disease (DKD) is one of the most common kidney diseases worldwide. It is estimated that approximately 537 million adults worldwide have diabetes, and up to 30%–40% of diabetic patients are at risk of developing nephropathy. The pathogenesis of DKD is complex, and its onset is insidious. Currently, the clinical diagnosis of DKD primarily relies on the increase of urinary albumin and the decrease in glomerular filtration rate in diabetic patients. However, the excretion of urinary albumin is influenced by various factors, such as physical activity, infections, fever, and high blood glucose, making it challenging to achieve an objective and accurate diagnosis. Therefore, there is an urgent need to develop an efficient, fast, and low-cost auxiliary diagnostic technology for DKD. In this study, an improved Dual Branch Attention Network (DBAN) was developed to quickly identify DKD. Serum Raman spectroscopy samples were collected from 32 DKD patients and 32 healthy volunteers. The collected data were preprocessed using the adaptive iteratively reweighted penalized least squares (airPLS) algorithm, and the DBAN was used to classify the serum Raman spectroscopy data of DKD. The model consists of a dual branch structure that extracts features using Convolutional Neural Network (CNN) and bottleneck layer modules. The attention module allows the model to learn features specifically, and lateral connections are added between the dual branches to achieve multi-level and multi-scale fusion of shallow and deep features, as well as local and global features, improving the classification accuracy of the experiment. The results of the study showed that compared to traditional deep learning algorithms such as Artificial Neural Network (ANN), CNN, GoogleNet, ResNet, and AlexNet, our proposed DBAN classification model achieved 95.4% accuracy, 98.0% precision, 96.5% sensitivity, and 97.2% specificity, demonstrating the best classification performance. This is the best method for identifying DKD, and has important reference value for the diagnosis of DKD patients, as well as improving the accuracy of medical auxiliary diagnosis. [Display omitted] • A new dual branch attention network (DBAN) is proposed. • The dual branch combines different attention mechanisms to achieve multi-level and multi-scale fusion. • The DBAN model achieves excellent classification performance for diabetic kidney disease diagnosis. [ABSTRACT FROM AUTHOR]

[Display omitted] • Differences in the biochemical composition of five types of cervical tissue were analyzed. • A multi-layered attention mechanism feature fusion architecture (MAFA) is proposed. • MAFA can effectively improve the classification accuracy of deep learning models for tissue samples. • MAFA as a model building method can be easily combined with a variety of deep learning models. Cervical cancer ranks among the most prevalent forms of gynecological malignancies. Timely identification of cervical lesions and prompt intervention can effectively prevent the development of cervical cancer or enhance patients' chances of survival. In this study, we propose an innovative method based on Raman spectroscopy, i.e., a multi-level SENet attention mechanism feature fusion architecture (MAFA) for rapid diagnosis of cervical cancer and precancerous lesions. The convolution process of this architecture can extract features from shallow to deep layers, and the attention mechanism is added to achieve the fusion of features from different layers. The added attention mechanism can automatically determine the importance of each layer feature channel and assign weight values to that layer according to the importance of each layer to achieve the purpose of focusing the model on certain waveform features and improve the targeting of model learning. We collected Raman spectra of 212 cervical tissues containing cervical cancer and its precancerous lesions.The experimental results show that MAFA can effectively improve the diagnostic accuracy of VGGNet, GoogLeNet and ResNet models in the validation of Raman spectral data of cervical tissue. Among them, ResNet performed the best, with the highest average accuracy, precision, recall and F1-Score of 82.36%, 84.00%, 82.35% and 82.26%, respectively, when no feature fusion was performed. The evaluation metrics improved by 4.91%, 3.97%, 4.97%, and 5.06%, respectively, after using the MAFA; they also improved by 4.16%, 2.90%, 4.17%, and 4.32%, respectively, compared with the model that directly performs feature fusion without using the attention mechanism. Therefore, the MAFA proposed in this study is better than that of the neural network that directly fuses the features of each convolutional layer. The experimental results show that the performance of the MAFA proposed in this paper is significantly higher than that of traditional deep learning algorithms, indicating that the present architecture can effectively improve the diagnostic accuracy of deep learning networks for cervical cancer. [ABSTRACT FROM AUTHOR]

The encoder–decoder structure has been introduced into semantic segmentation to improve the spatial accuracy of the network by fusing high- and low-level feature maps. However, recent state-of-the-art encoder–decoder-based methods can hardly attain the real-time requirement due to their complex and inefficient decoders. To address this issue, in this paper, we propose a lightweight bilateral attention decoder for real-time semantic segmentation. It consists of two blocks and can fuse different level feature maps via two steps, i.e. , information refinement and information fusion. In the first step, we propose a channel attention branch to refine the high-level feature maps and a spatial attention branch for the low-level ones. The refined high-level feature maps can capture more exact semantic information and the refined low-level ones can capture more accurate spatial information, which significantly improves the information capturing ability of these feature maps. In the second step, we develop a new fusion module named pooling fusing block to fuse the refined high- and low-level feature maps. This fusion block can take full advantages of the high- and low-level feature maps, leading to high-quality fusion results. To verify the efficiency of the proposed bilateral attention decoder, we adopt a lightweight network as the backbone and compare our proposed method with other state-of-the-art real-time semantic segmentation methods on the Cityscapes and Camvid datasets. Experimental results demonstrate that our proposed method can achieve better performance with a higher inference speed. Moreover, we compare our proposed network with several state-of-the-art non-real-time semantic segmentation methods and find that our proposed network can also attain better segmentation performance. • We propose a refinement block to improve the information capturing ability of feature maps. • We develop a pooling fusion block to take full advantages of different level feature maps. • Combining two blocks, we propose a lightweight decoder for real-time semantic segmentation. • Our method can attain state-of-the-art accuracy and speed on Cityscapes and Camvid datasets. [ABSTRACT FROM AUTHOR]

Normalized difference vegetation index (NDVI), derived from multi-spectral (MS) images, is a metric widely used to evaluate the growth status of vegetation in remote sensing. Existing methods for generating high-resolution (HR) NDVI are typically based on pan-sharpening, which often result in huge errors even in case of tiny spectral distortions. To overcome this challenge, from a novel perspective, this paper introduces an HR vegetation index (HRVI) to realize direct fusion with a low-resolution NDVI rather than pan-sharpening an HRMS image. In particular, we propose a two-branch network based on the multi-scale and attention mechanism, termed as NDVI-Net, to obtain the HRNDVI with small distortion. In our network, the multi-scale channel enhancement blocks are used in both NDVI and HRVI branches, in which multi-scale convolution is used to capture structural information with different reception fields and channel attention mechanism is adopted to perform feature selection. Meanwhile, the spatial features are injected unidirectionally from the HRVI into NDVI branches, so as to further improve the quality of features in the NDVI branch. Subsequently, the spatial intensify block is adopted only in the NDVI branch to implement selective enhancement for the previously obtained features along the spatial position, strengthening the retention of local detail features. Finally, HRNDVI is reconstructed based on the high-representation NDVI features, which contains clear texture details and precise intensity. Experimental results demonstrate the significant advantage of our method over the current state-of-the-art in terms of both subjective visual effect and quantitative metrics. Moreover, we apply the HRNDVI generated by our method to vegetation detection and enhancement, and land cover mapping in remote sensing, which can achieve the best performance. [ABSTRACT FROM AUTHOR]

Deep learning-based breast lesion detection in ultrasound images has demonstrated great potential to provide objective suggestions for radiologists and improve their accuracy in diagnosing breast diseases. However, the lack of an effective feature enhancement approach limits the performance of deep learning models. Therefore, in this study, we propose a novel dual global attention neural network (DGANet) to improve the accuracy of breast lesion detection in ultrasound images. Specifically, we designed a bilateral spatial attention module and a global channel attention module to enhance features in spatial and channel dimensions, respectively. The bilateral spatial attention module enhances features by capturing supporting information in regions neighboring breast lesions and reducing integration of noise signal. The global channel attention module enhances features of important channels by weighted calculation, where the weights are decided by the learned interdependencies among all channels. To verify the performance of the DGANet, we conduct breast lesion detection experiments on our collected data set of 7040 ultrasound images and a public data set of breast ultrasound images. YOLOv3, RetinaNet, Faster R-CNN, YOLOv5, and YOLOX are used as comparison models. The results indicate that DGANet outperforms the comparison methods by 0.2%-5.9% in total mean average precision. [ABSTRACT FROM AUTHOR]

Rain streaks in the air show diverse characteristics with different shapes, directions, densities, even the complex overlapped phenomenon, causing great challenges for the deraining task. Recently, deep learning based image deraining methods have been extensively investigated due to their excellent performance. However, most of the existing algorithms still have limitations in removing rain streaks while preserving rich textural details under complicated rain conditions. To this end, we propose to decompose rain streaks into multiple rain layers and individually estimate each of them along the network stages to cope with the increasing abstracts. To better characterize rain layers, an improved non-local block is designed to exploit the self-similarity of rain information by learning the holistic spatial feature correlations while reducing the calculation complexity. Moreover, a mixed attention mechanism is applied to guide the fusion of rain layers by focusing on the local and global overlaps among these rain layers. Extensive experiments on both synthetic rainy/rain-haze/raindrop datasets, real-world samples, the haze, and low-light scenarios show substantial improvements both on quantitative indicators and visual effects over the current state-of-the-art technologies. The source code is available at https://github.com/kuihua/IADN. [ABSTRACT FROM AUTHOR]

• Propose a deep-learning intelligent system incorporating data augmentation for STVS assessment. • Build a BiGRU-attention-based assessment model to extract temporal dependencies. • Effectiveness and superiority of the approach is verified by the New England 39-bus system. • Statistical tests have been performed to examine the performance of the proposal. Facing the difficulty of expensive and trivial data collection and annotation, how to make a deep learning-based short-term voltage stability assessment (STVSA) model work well on a small training dataset is a challenging and urgent problem. Although a big enough dataset can be directly generated by contingency simulation, this data generation process is usually cumbersome and inefficient; while data augmentation provides a low-cost and efficient way to artificially inflate the representative and diversified training datasets with label preserving transformations. In this respect, this paper proposes a novel deep-learning intelligent system incorporating data augmentation for STVSA of power systems. First, due to the unavailability of reliable quantitative criteria to judge the stability status for a specific power system, semi-supervised cluster learning is leveraged to obtain labeled samples in an original small dataset. Second, to make deep learning applicable to the small dataset, conditional least squares generative adversarial networks (LSGAN)-based data augmentation is introduced to expand the original dataset via artificially creating additional valid samples. Third, to extract temporal dependencies from the post-disturbance dynamic trajectories of a system, a bi-directional gated recurrent unit with attention mechanism based assessment model is established, which bi-directionally learns the significant time dependencies and automatically allocates attention weights. The test results demonstrate the presented approach manages to achieve better accuracy and a faster response time with original small datasets. Besides classification accuracy, this work employs statistical measures to comprehensively examine the performance of the proposal. [ABSTRACT FROM AUTHOR]

Developing monitoring system for paper industrial wastewater treatment system is an important route for wastewater reuse and recycling from wastewater, which are regarded as effective way for cleaner production. A novel hybrid deep leaning CLSTMA model, which based on sequential fusion convolutional neural network (CNN), long short term memory (LSTM) and attention mechanism (AM), was developed to monitor the water quality in a full-scale paper industrial wastewater treatment system for energy conservation and emissions reduction. The hybrid CLSTMA model for predicting water quality of paper industrial wastewater treatment system was divided into three steps: spatial information fusion by using CNN module, temporal information fusion by using LSTM module and variable weighted calculation by using AM module. Compare with other models (CNN, LSTM and CLSTM models), RMSE of CLSTMA model for the effluent chemical oxygen demand (COD eff) reduced by 23.3–31.55%, MAE of CLSTMA model reduced by 38.89–74.50%, R of CLSTMA model increased by 8.29–11.86%. For the effluent suspended solids (SS eff), compared with CNN and LSTM models, RMSE of CLSTMA model reduced by 10.26% and 9.92%, MAE of CLSTMA model reduced by 5.37% and 3.44%, R of CLSTMA model increased by 15.13% and 37.21%, respectively. While, R of CLSTMA was consistent with CLSTM model, but RMSE and MAE of CLSTMA model reduced by 16.07% and 7.49% than the CLSTM model. Simulation results demonstrate that the proposed CLSTMA model has a great potential in monitoring paper industrial wastewater treatment system for cleaner production. [Display omitted] • A new deep learning model was proposed for real-time estimation of water quality. • The new model was based on convolutional neural network, long-term short-term memory and attention mechanism. • The new model can guarantee wastewater reuse and reduce the wastewater treatment cost. [ABSTRACT FROM AUTHOR]