Search

• A staged data preprocessing strategy for honey peach young fruit is designed. • The SCAE is proposed to ensure the discriminability of the extracted features. • The SCAE is embedded into the DeepSORT to form the DeepSORT-SCAE approach. • The DeepSORT-SCAE can measure the spatially continuous trajectory of the young fruit. • The effectiveness of the developed method is verified by experiments. The young fruit bagging robot usually preferentially selects the object with largest prediction box size for bagging. However, due to the uncertainty in the output of the traditional models, the expected endpoint of mechanical motion may change constantly, which affects the bagging efficiency. This paper proposes a novel tracking algorithm for honey peach young fruit targets. Firstly, according to the proportion of young fruit prediction box in the original image, a staged data preprocessing strategy is designed. Then, the siamese convolution autoencoder (SCAE) is developed by combining autoencoding mechanism with siamese training mode, and the SCAE is trained in two-stages to ensure the discriminability of the extracted appearance features. Finally, the SCAE is inserted into the DeepSORT architecture to generate the DeepSORT-SCAE object tracking algorithm, which can measure the spatially continuous trajectory of multiple young fruit targets within video streams. The effectiveness of the developed method is verified by experiments. [ABSTRACT FROM AUTHOR]

Deep learning-based fault diagnosis methods have to be trained by a large amount of labeled data for accurate diagnosis results. However, in real-case industrial scenarios, the data available is limited since the difficulty of obtaining fault samples. In addition, the monitoring data always come from various working conditions. As a result, data scarcity and domain shift have become two main factors that limit the performance of diagnosis models. We called this problem the few-shot cross-domain fault diagnosis, which has been rarely explored. To deal with the above challenge, we propose a novel uncertainty-based contrastive prototype-matching network (UCPMnet). Specifically, two main parts are conducted for UCPMnet. Considering the construction of representation spaces for compact intra-class and sparse inter-class embeddings, the contrastive prototypical network (CPN) is established by remolding the prototypical network. Subsequently, an uncertainty-based prototype-matching (UPM) method is presented for class-wise domain adaptation based on pseudo-label learning. At the same time, an estimation approach of prediction uncertainty is introduced to adaptively rectify the threshold for screening the pseudo label, which aims to mitigate the effect of noisy predictions for prototype-matching. Extensive experiments are conducted on two datasets, and four classical methods are selected for comparison. The results corroborate the superiority of the UCPMnet for fault diagnosis in the case of small data. [ABSTRACT FROM AUTHOR]

[Display omitted] • The attention mechanism implements adaptive feature non-linear transformation. • A hyperparameter adaptive activation function is developed based on tanh function. • Designed activation function avoids non-zero mean and gradient vanishing problems. • A deep adaptive network based on ResNet is proposed for bearing fault diagnosis. • Comparative experiments verify the superiority of proposed fault diagnosis model. Bearing health condition directly affects the reliability of mechanical equipment. Although deep learning (DL) algorithms have achieved great results in the field of bearing fault diagnosis, traditional activation function uses a fixed mathematical formula to achieve non-linear feature transformation, which tend to compress part of the effective fault information and reduce the performance of fault diagnosis. To address this problem, this paper proposes a slope and threshold adaptive activation function with tanh function (STAC-tanh). Establish the relationship between non-linear feature transformation and input signal by automatically adjusting the shape of activation function. Finally, the model can retain valid fault information to improve fault diagnosis performance. Then, combining STAC-tanh and Residual Networks, this paper proposes ResNet-STAC-tanh for bearing fault diagnosis. Experimenting on the two bearing datasets with added noise, the average accuracy of the network reached 90.00% and 90.77%, respectively. The effectiveness of the new method was verified through comparative experiments. [ABSTRACT FROM AUTHOR]

Ultrasound has become an indispensable clinical scanning and diagnostic tool because of its convenience, rapidity, and radiation-free properties. In ultrasound imaging, the resolution and detection depth of ultrasound images are closely related to the frequency of the transmitted ultrasound. Ultrasound with low transmission frequency has a wide detection range, but its images have problems such as low resolution, blurred edge contours and obvious background noise. In contrast, the higher transmission frequency, the smaller the detection range, but the resolution has improved. The rapid development of deep learning provides new approaches and enormous potential for improving resolution of ultrasound images and acquiring high-resolution images with an extensive detection range. In this paper, the attention mechanism is combined into a convolutional neural network based on the encoder-decoder, and the feature maps extracted at different scales are merged by skip connections to preserve more structure and contrast details, thus realizing the improvement of lower-frequency ultrasound image resolution. This work utilizes the capacitive micromechanical ultrasound transducer (CMUT) probe for dataset acquisition. The network's performance is validated by simulated datasets for point targets and experimental datasets for breast body models. The experimental results show that for ultrasound images, the network output map can improve by 0.2 in structural similarity and at least 11 dB in peak signal-to-noise ratio. The proposed network can effectively preserve ultrasound images' structural and textural information, improve edge contour definition, and significantly suppress noise and imaging artifacts, which has significant development potential in ultrasound imaging. [Display omitted] • Different ultrasound emission frequencies have significant impact on imaging quality. • Datasets of different frequencies using capacitive micromechanical ultrasound transducer (CMUT) probes. • Using deep learning to improve the quality of low-frequency images by extracting features from high-frequency images. • With incorporation of the attention mechanism, the network highlights the prominent features through skip connections while maintaining computational efficiency. [ABSTRACT FROM AUTHOR]

Agricultural drought seriously threatens the food and ecological security of most of the world's developing countries. Data-driven integrated agricultural drought index with remote sensing provides an effective tool to monitor, evaluate, and predict the agricultural drought. However, there is still a lack of comprehensive analytical work on taking the most effective machine learning (ML) and deep learning (DL) methods to construct such integrated drought index. In other words, it is still unclear whether the recent DL methods can improve integrated drought monitoring as compared with the currently widely used ML methods. Therefore, we critically evaluated the performances of four representative DL methods (represents the four currently popular DL network types) i.e., Entity Embedding Deep Neural Network (EEDNN), One-dimensional Convolutional Neural Network (1D-CNN), Gated Recurrent Unit (GRU), and Self-Attention Mechanism (SAM) and three widely used tree-based ML methods i.e., Cubist, Random Forest (RF), and Light Gradient Boosting Machine (LGBM), through constructing a QuickDRI like integrated drought index (abbreviated as QuickDRI-China). About 30 years of meteorological data, 14 years of remote sensing data, and various biophysical variables in China such as land use/land cover, available water capacity, irrigated agriculture, elevation, and ecoregion were employed in this study. Results showed that the EEDNN performed best, followed by the RF and LGBM, and then the other methods including the currently wide used Cubist, according to the station accuracy evaluations, spatial description evaluations, and responses to specific drought event. The tree-based ML methods such as RF and LGBM are still competitive in constructing the integrated agricultural drought index at the current stage. However, the higher accuracy, the smoother spatial description, and the more responsive ability of the EEDNN demonstrate great potential of DL methods. The future integrated agricultural drought monitoring with remote sensing should develop a specialized DL network for heterogeneous agricultural drought features. • Various deep learning approaches were examined for integrated drought monitoring. • Tree-based ensemble learning methods were effective. • Deep learning has unique advantages for assessing spatial description of agricultural drought. • Deep learning has great potential to improve integrated drought monitoring. [ABSTRACT FROM AUTHOR]

In image denoising, the recovery of high-frequency regions, such as image edges, directly affects the quality of the denoised images. However, previous deep learning-based denoising methods fail to effectively allocate the transmission of different frequency information and have difficulty giving network attention to high-frequency regions. In this paper, we rethink the fusion of image gradients in a neural network and deeply mine the intrinsic structure of the input image to propose a novel layered input gradient network (LIGN) for image denoising. The core of our network focuses on the features of different frequencies through two networks, which contain several key elements: (a) The input noise image is layered to widen the shallow layer of the network and to promote the hierarchical learning of different types of frequencies. (b) A multiscale feature extraction (MFE) block and information shunting (IS) block are proposed to integrate and separate various frequency features. (c) A gradient network (GradiNet) is designed to extract high-frequency information by network training, and the information is adaptively added to the input of the parallel main network (MainNet) through normalization to obtain high-quality images. Furthermore, we propose a sharpening loss function to enhance the texture details of the denoised image and improve visual quality. Extensive experiments on synthetic and real-world datasets show that the proposed method greatly enhances perceptual visual quality and achieves state-of-the-art performance on both PSNR and SSIM. The source code and pretrained models are available at https://github.com/JerryYann/LIGN. • A layered input gradient network (LIGN) based on a dual U-Net for high-quality image denoising is proposed. • Layered input and sharpening loss greatly improve the perceptual quality of the denoised image. • Multi-scale feature extraction block can capture more semantic information. • LIGN achieves the SoTA performance compared with the latest methods on synthetic and real noise datasets. [ABSTRACT FROM AUTHOR]

• A study on the use of deep learning model in the field of neutron image restoration is performed. • A lightweight, convenient and efficient deep learning network is proposed for neutron image restoration. • The X-ray dataset solves the problem of lack of real neutron image datasets. • The pre-training model achieves good results in real neutron image restoration. Traditional neutron image restoration (NIR) methods lack flexibility and generalization in dealing with multiple image restoration tasks, which indirectly limits the practical applications of neutron images. Deep convolutional neural networks (CNNs) have achieved impressive success in image restoration, so this paper preliminarily explores and applies deep CNNs in NIR. Considering the characteristics and applications of neutron images, we propose a fast and lightweight densely connected attention U-Net (DAUNet) for NIR. We strengthen the network's attention to the image structural information and the processing at different frequencies by adding an attention mechanism to the symmetric skip connection under each scale of U-Net. The proposed DAUNet uses a single model for recovering images with different degradations, such as additive noise and blur. Extensive experiments on synthetic and real neutron-degraded images are conducted, and the results show that DAUNet is effective and efficient in NIR and achieves satisfactory results in terms of both comprehensive performance (PSNR) and intrinsic metrics (parameters, memory cost and running time), which are highly attractive for practical applications. In addition, to address the problem of lacking real NIR datasets, we use X-ray images that the imaging principles are closer to that of neutron imaging as data drivers for the network. [ABSTRACT FROM AUTHOR]