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Accurate inferior alveolar nerve (IAN) canal segmentation has been considered a crucial task in dentistry. Failing to accurately identify the position of the IAN canal may lead to nerve injury during dental procedures. While IAN canals can be detected from dental cone beam computed tomography, they are usually difficult for dentists to precisely identify as the canals are thin, small, and span across many slices. This paper focuses on improving accuracy in segmenting the IAN canals. By integrating our proposed frequency-domain attention mechanism in UNet, the proposed frequency attention UNet (FAUNet) is able to achieve 75.55 % and 81.35 % in the Dice and surface Dice coefficients, respectively, which are much higher than other competitive methods, by adding only 224 parameters to the classical UNet. Compared to the classical UNet, our proposed FAUNet achieves a 2.39 % and 2.82 % gain in the Dice coefficient and the surface Dice coefficient, respectively. The potential advantage of developing attention in the frequency domain is also discussed, which revealed that the frequency-domain attention mechanisms can achieve better performance than their spatial-domain counterparts. [ABSTRACT FROM AUTHOR]

[Display omitted] • The spectra-image features of defective maize kernels were obtain by HSI. • CNN and spectral-spatial attention were combined to recognize the HSI features. • The CNN-Spl-Spal-At based on fusion features had the excellent performance. • The identification results of various defective kernels were visualized. Rapid, effective and non-destructive detection of the defective maize kernels is crucial for their high-quality storage in granary. Hyperspectral imaging (HSI) coupled with convolutional neural network (CNN) based on spectral and spatial attention (Spl-Spal-At) module was proposed for identifying the different types of maize kernels. The HSI data within 380–1000 nm of six classes of sprouted, heat-damaged, insect-damaged, moldy, broken and healthy kernels was collected. The CNN-Spl-At, CNN-Spal-At and CNN-Spl-Spal-At models were established based on the spectra, images and their fusion features as inputs for the recognition of different kernels. Further compared the performances of proposed models and conventional models were built by support vector machine (SVM) and extreme learning machine (ELM). The results indicated that the recognition ability of CNN with attention series models was significantly better than that of SVM and ELM models and fused features were more conducive to expressing the appearance of different kernels than single features. And the CNN-Spl-Spal-At model had an optimal recognition result with high average classification accuracy of 98.04 % and 94.56 % for the training and testing sets, respectively. The recognition results were visually presented on the surface image of kernels with different colors. The CNN-Spl-Spal-At model was built in this study could effectively detect defective maize kernels, and it also had great potential to provide the analysis approaches for the development of non-destructive testing equipment based on HSI technique for maize quality. [ABSTRACT FROM AUTHOR]

It is significant to identify the moldy status of stored maize by fungi infection in the early stage. Hyperspectral imaging (HSI) combined with the sparse auto-encoders (SAE) and convolutional neural network (CNN) algorithms was used to classify the moldy grades of maize kernels. The HSI data were obtained in the range of 400–1000 nm, and four grades from health to heavy mildew were distinguished using the measured fungal spores of maize. The depth spectral features were represented using SAE and the image features were extracted by CNN. K nearest neighbors, support vector machine (SVM), and partial least squares discriminant analysis classifiers were combined with the spectral and image features to establish classification models to identify the different moldy grades of maize kernels. The comparison results indicated that the fusion of SAE and CNN combined with the SVM classifier to construct the SAE-CNN-SVM model had the most satisfactory identification result with high correct recognition rates of 99.47% and 98.94% for the training and testing sets, respectively, and the values of sensitivity and specificity were 0.95–1. The moldy grades were presented intuitively on the maize image based on pixels or kernel-wise. Therefore, the HSI with the SAE-CNN-SVM model had good recognition ability for the early detection of moldy maize kernels, which could potentially provide technical support for the development of online detection of moldy maize kernels during storage. [ABSTRACT FROM AUTHOR]

The classification of lower limb gait phase is very important for the control of exoskeleton robots. In order to enable the exoskeleton to determine gait phase and provide appropriate assistance to the wearer, we propose a compound network based on CNN-BiLSTM. The method uses data from inertial measurement units placed on the leg and pressure sensor arrays placed on the sole as inputs to the model. The convolutional neural network (CNN) is used to obtain the local key features of gait data, and then the bidirectional long short-term memory (BiLSTM) network is used to extract the serialized gait phase information from the local key features to obtain the high-level feature expression. Finally, the seven phases of both feet were obtained through the classification of the softmax layer. We designed a gait acquisition system and collected the gait data from seven subjects at varying walking speeds. In the test set, the highest gait phase classification accuracy can reach 95.09%. We compared the proposed model with the long short-term memory (LSTM) network and gated recurrent unit (GRU) network. The experimental results show that the average accuracy of CNN-BiLSTM network from seven subjects is 0.417% higher than that of the LSTM network and 0.596% higher than that of the GRU network. Therefore, the ability of the CNN-BiLSTM network to classify gait phases can be applied in designing exoskeleton controllers that can better assist for different gait phases correctly to assist the wearer to walk. [ABSTRACT FROM AUTHOR]

• The temperature distribution on weldment sidewall exhibits a certain degree of separability under different offset degrees of cladding layer. • A cladding layer offset recognition method based on infrared sensing and deep learning for WAAM is proposed. • The proposed method effectively solves the problem of arc light interference in the cladding layer offset recognition method based on visual perception. In the wire arc additive manufacturing (WAAM) procedure, the stability of the cladding layer's formation directly impacts the final dimensional precision of the WAAM weldment. To achieve high-precision forming of the WAAM weldment, online monitoring of the cladding layer offset is essential. This article explores the monitoring technology for the cladding layer offset in the WAAM process and introduces a cladding layer offset monitoring technique using infrared temperature measurement and deep learning. First, a temperature field measurement system for the WAAM weldment is set up using an infrared thermal imager. Next, a region of interest (ROI) is selected on the weldment sidewall. The optimised histogram of oriented gradient (HOG) algorithm is then employed to extract the temperature distribution characteristics of the ROI. The separability of the temperature field on the weldment sidewall is analysed under various offset degrees of the cladding layer. Finally, using the temperature image of the ROI as input, a cladding layer offset recognition model is developed using a convolutional neural network (CNN). This study also evaluates the effect of the selected ROI position on the recognition precision of the model for the cladding layer offset. Experimental results indicate that the recognition accuracy of the model for cladding layer offset, as presented in this article, exceeds 99%. Moreover, the model demonstrates strong robustness, further enriching the intelligent monitoring methods for the WAAM process. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have been widely used for synthetic aperture radar (SAR) target detection. Typical methods based on CNN have obtained favorable detection accuracy at the cost of high model complexity, and thus are difficult to be directly applied to real-time satellites on board as well as maritime rescue. To deal with this problem, this article proposes an efficient and lightweight target detection network incorporating soft quantization. First, to compensate for the lack of accuracy caused by lightweight networks, a feature fusion module called split bidirectional feature pyramid network is proposed to alleviate the interference of complex background on SAR images. Meanwhile, to adapt the lightweight network and the feature fusion module, a linear transformation module is presented to enhance the linear representation of the model via learnable parameters. Eventually, to make the model size smaller, a soft quantization algorithm is proposed to reduce the accuracy degradation caused by quantization errors. We validate the robustness of the model in several publicly available datasets. Experimental results show that our model achieves 97.0% detection accuracy on SAR ship detection dataset, with a 0.9% accuracy improvement compared to mainstream methods using less than $15\times $ the number of parameters and less than $6\times $ the number of flops. [ABSTRACT FROM AUTHOR]

Highlights • We propose an automatic suspicious object detection and recognition method for THz security images. • We employed the advanced CNN model to achieve intelligent analysis. • The spatio-temporal context information is explored via SLD model for performance refinement. Abstract Owing to low radiation required for human body, complete detection of hazardous materials, noncontact privacy protection, and fast pass capability, terahertz (THz) security screening cameras (TSSCs) are widely deployed in public places. However, the existing TSSCs only report about the rough locations of suspicious objects, while directing the complicated detailed recognition work to security inspectors, thus leading to low recognition efficiency. In this paper, we aim to introduce the artificial intelligence technique, called convolutional neural network (CNN), with the spatio-temporal information of THz security image sequences, to achieve automatic object detection and recognition. Our method is composed of rough detection and detailed recognition. In rough detection, benefiting from the inherent alignment of human parts, the sparse and low-rank decomposition (SLD) is used to excavate the spatio-temporal context information. Specifically, the low-rank part representing the static background is regarded as the human body, while the sparse part representing the displaced target is regarded as the suspicious object. Then, by considering the shape knowledge and performing morphological processing, noise interference was reduced and the rough locations of suspicious objects were determined. In detailed recognition, supervised training was first conducted based on Faster R-CNN model with large-scale object labels. The trained Faster R-CNN could extract high-level semantic features of each anchor, and thus predict the class attribute of each object. Notably, with the results of rough detection, only anchors in the candidate domain are computed. Compared with the conventional full domain computation, this narrow-band approach not only reduces the computational complexity but also decreases the false positives caused by anchors in the background. Extensive experiments were conducted on THz security images, and the results prove that our method achieves high performance with respect to both accuracy and efficiency. [ABSTRACT FROM AUTHOR]

• A method is proposed for damage identification under nonstationary excitations. • UARDM is a new type of RP that represents dynamic characteristics of the structure. • Multi-label CNN model decouples the identification of damage locations and levels. • The proposed method performs with higher identification accuracy and efficiency. Civil engineering structures inevitably suffer from nonstationary ambient excitations in practice, which make conventional damage identification methods relying on the stationary assumption ineffective. This study presents a novel method based on unthresholded assembled recurrence distance matrix (UARDM) and multi-label convolutional neural network (CNN) for structural damage identification under nonstationary excitations. UARDM is a new type of recurrence plot (RP) that is proposed to integrate information of multiple channels and dispense with the artificially selected threshold. It reveals intrinsic dynamic characteristics of the structure using its vibration responses from the perspective of global probabilistic autocorrelation. After that, CNN is applied to automatically extract damage-sensitive features of UARDMs and classify them for the identification of damage cases. Instead of the traditional single-label CNN model that labels each combination of damage location and level as an objective class, the multi-label CNN model is developed to decouple the identification processes of damage locations and levels in order to improve the identification accuracy and computational efficiency. It evaluates the damage level at each location through a sub-branch with an independent set of labels and detects the damage locations by fusing information of all the sub-branches. A comprehensive comparison was conducted among single-label and multi-label CNN models input with raw accelerations, unthresholded multivariate recurrence plots (UMRPs), unthresholded recurrence plots (URPs) and UARDMs through numerical simulation and experimental test. It was demonstrated that the proposed structural damage identification method based on UARDM and multi-label CNN was able to identify multiple damage locations and levels under various stationary and nonstationary excitations with higher accuracy, efficiency and robustness, and even able to detect multiple-damage cases that were not measured beforehand and involved in the training dataset. [ABSTRACT FROM AUTHOR]