Pavement crack defect detection is an important task in road maintenance. Accurate detection of crack defects has far-reaching significance in maintaining the health condition of roads. Although many excellent crack defect detection algorithms have emerged, the detection effect on the edge details of the crack defects is still not ideal. In this paper, we propose a novel global-local fusion network based on edge enhancement and Transformer for pavement crack defect segmentation. Aiming at the structural characteristics of the pavement crack defects, combined with the edge detection algorithm (Sobel), an edge feature enhancement (EFE) module is presented to realize the accurate extraction of local detail information of the pavement crack defects. Meanwhile, a Transformer-based encoding path is also built to extract rich global information. Faced with the two different types of feature information, an adaptive fusion (AF) module is proposed to realize the efficient fusion of the two types of feature information. Furthermore, an attention-based local feature enhancement (ALFE) module and an edge refinement module (ER) are proposed to further suppress the interference in the local feature maps and refine the edge features of the pavement crack defects. Finally, a multi-scale feature enhancement (MFE) module is presented for multi-scale attention feature representation, by which we can provide high-quality input features for the decoding side. After extensive experimental validation, our proposed model has demonstrated a superior performance over existing mainstream models on multiple pavement crack defect segmentation datasets. The code of the model has been open to:https://github.com/MMYZZU/Crack-Segmentation.

Minimally invasive surgery, which relies on surgical robots and microscopes, demands precise image segmentation to ensure safe and efficient procedures. Nevertheless, achieving accurate segmentation of surgical instruments remains challenging due to the complexity of the surgical environment. To tackle this issue, this paper introduces a novel multiscale dual-encoding segmentation network, termed MSDE-Net, designed to automatically and precisely segment surgical instruments. The proposed MSDE-Net leverages a dual-branch encoder comprising a convolutional neural network (CNN) branch and a transformer branch to effectively extract both local and global features. Moreover, an attention fusion block (AFB) is introduced to ensure effective information complementarity between the dual-branch encoding paths. Additionally, a multilayer context fusion block (MCF) is proposed to enhance the network's capacity to simultaneously extract global and local features. Finally, to extend the scope of global feature information under larger receptive fields, a multi-receptive field fusion (MRF) block is incorporated. Through comprehensive experimental evaluations on two publicly available datasets for surgical instrument segmentation, the proposed MSDE-Net demonstrates superior performance compared to existing methods.

Currently, cracks are the most common defect in pavement diseases. Long-term non-maintenance can lead to crack lengthening and expansion, causing serious traffic accidents, as well as shortening the service life of pavement cracks. Therefore, it is of utmost importance to maintain cracks at an early stage. Due to the effect of some challenging factors, such as various shape information of the cracks, complex textured backgrounds, light shadows, similar texture objects, micro cracks and other factors, accurate crack detection still faces a certain challenges. To solve the above problems, a dual-encoding multi-scale fusion network based on the combination of convolutional neural network (CNN) and transformer network is proposed, named DMF-Net. To obtain stronger feature representations, a dual-encoding path is built to acquire global context features and local detail information simultaneously, where global context features are extracted based on the transformer branch, and the local detail features are extracted based on the CNN branch to detect tiny details of the cracks. Meanwhile, an interactive attention learning (IAL) module is introduced to effectively fuse the global features from the transformer branch and the local detail information from the CNN branch, achieving mutual communication and learning of different feature information. In addition, to enrich the feature representation ability, an attention-based feature enhancement (AFE) module is introduced to acquire more global contexts. Furthermore, faced with the crack detection task with class imbalance issue, a triple attention module (TAM) is built to emphasize the micro cracks. Finally, in the segmentation prediction stage, the deep supervision mechanism is also introduced to accelerate the convergence speed of the model, and serve effective multi-scale feature fusion. Compared with the current mainstream segmentation models, excellent performance has been obtained, which could provide a feasible scheme for the early maintenance of pavement cracks. The source code about proposed DMF-Net is available at https://github.com/Bsl1/DMFNet.git.

Pavement crack detection poses a formidable challenge due to the intricate texture structures of cracks and the complex environmental settings in which they are situated. In recent years, the advancement of deep learning techniques has prompted a surge in the utilization of Convolutional Neural Network (CNN)-based methods for pavement crack detection. While CNNs have exhibited remarkable results in crack detection tasks, they primarily excel at capturing local details with limited receptive fields, which can be insufficient for grasping global contextual information. Given the intricate nature of crack textures, it becomes imperative to leverage both global and local features for accurate detection. To address this issue, a transformer-based network with feature complementary fusion, refer to TFCF-Net, is introduced, which amalgamates Transformer and CNN architectures. Proposed TFCF-Net model prioritizes the Transformer branch for feature encoding, considering its strength in extracting global features, while the CNN branch is set as auxiliary encoding branch, which plays a complementary role for local feature extraction. Proposed TFCF-Net operates by utilizing global features as a foundation and iteratively refining them using local features, thus facilitating precise crack detection. This design enables proposed network to comprehensively capture both global and local information while judiciously fusing these two types of information based on the distinctive characteristics of cracks. To ensure effective fusion of global and local information, an Information Complementary Fusion (ICF) module is presented, which could efficiently merge the outputs of both encoding branches. To further optimize the fused information, a multi-dimensional attention (MA) module is proposed to embed into the, which enhances the model’s ability to capture long-range dependencies by optimizing information from multiple dimensions. Additionally, to improve the quality of input features on the decoding side, a multi-dimensional attention feature representation (MAFR) module is proposed, which expands the receptive field of the deepest semantic information, enabling the extraction of multi-scale feature representations. This paper rigorously evaluate proposed TFCF-Net against state-of-the-art (SOTA) models using three publicly available pavement crack datasets. Experimental results unequivocally demonstrate the superior performance of the proposed TFCF-Net.

Automatic crack detection remains challenging due to factors such as irregular crack shapes and sizes, uneven illumination, complex backgrounds, and image noise. Deep learning has shown promise in computer vision for pixel-wise crack detection, but existing methods still suffer from limitations such as information loss, insufficient feature fusion, and semantic gap issues. To address these challenges, a novel pavement crack segmentation network, called PAF-Net, is proposed, which incorporates progressive and adaptive feature fusion. To mitigate information loss caused by feature downsampling, a progressive context fusion (PCF) block is introduced to capture context information from adjacent scales. To better capture strong features from local regions, a dual attention (DA) block is proposed that leverages both global and local context information, reducing the semantic gap issue. Furthermore, to achieve effective multi-scale feature fusion, a dynamic weight learning (DWL) block is proposed that enables efficient fusion of feature maps from different network layers. Additionally, a multi-scale input unit is incorporated to provide the proposed segmentation network with more contextual information. To evaluate the performance of PAF-Net, we conduct experiments using four common evaluation metrics and compare it with multiple mainstream segmentation models on three public datasets. The proposed PAF-Net demonstrates superior segmentation accuracy for pixel-level crack detection compared to other segmentation models, as evident from qualitative and quantitative experimental results.

Insulator fault diagnosis is a daily but key task for the power transmission system. Long-term exposure to complex natural environment will cause different insulator defects. As a common defects, missing-cap defects of insulators will not only affect the structural strength of power insulators, but also bring a certain effect to the stable power transmission. With the rapid development of machine learning, some machine learning-based defect recognition methods have been proposed for fast and high-precision power inspection. However, the handcrafted features could not effectively express the aerial images against complex inspection environment to affect detection performance of the shallow learning algorithms. And the detection precision of deep learning algorithms will be affected by the unbalanced small-scale defects. Therefore, the fast and high-precision power inspection still faces a certain challenge in the smart grid. To address the above issues, fusion with the deep convolutional neural network (DCNN) and transfer learning, a novel fault diagnosis algorithm of power insulators is proposed to provide a fast and accurate power inspection scheme. To remove complex backgrounds, a fast insulator location algorithm based on the lightweight YOLOV4 model is proposed which is served for the following defect recognition. On the basis, to imitate human vision, a defect recognition algorithm is proposed based on multi-feature fusion. Meanwhile, to ensure the feature expression ability of transfer learning on power insulators, a novel optimization strategy of transfer learning is proposed to improve the recognition precision. Experiments show that the proposed method could acquire a good recognition performance than other recognition models.

Dynamic gesture recognition is a typical human-computer interaction method owing to its great potential in practical applications. Currently, most of research work on gesture recognition has mainly focused on vision-based and surface electromyography (sEMG) methods. Compared to vision-based methods, the sequential sEMG signal can directly depict the muscle activity of different gestures which could lead to higher recognition efficiency. However, the effective feature design and selection of sEMG signal is still complicated since muscle fatigue and small electrode displacement will affect the recognition precision of sEMG signals. In this paper, a novel end-to-end dynamic gesture recognition method is developed. The raw sEMG signals are converted into an image form by using the time-frequency transformation method to obtain more comprehensive information for model training and test. And a recognition model based on Convolutional Neural Network (CNN) model is built for high-precision time-frequency image recognition. Experiments indicate that the proposed method could acquire distinguishing features from the pre-prossed images and the overall recognition accuracy on different gestures can reach up to 98.3%.

The hybrid-structured flexible manipulator has a complex structure and strong coupling between state variables. Meanwhile, the natural frequency of the hybrid-structured flexible manipulator varies with the motion of the telescopic joint, so it is difficult to suppress the vibration quickly. In this article, the tip state signal of the hybrid-structured flexible manipulator is decomposed into elastic vibration signal and tip vibration equilibrium position signal, and a combined control method is proposed to improve tip positioning accuracy and trajectory tracking accuracy. In the proposed combined control method, an improved nominal model-based sliding mode controller (NMBSMC) is used as the main controller to output the driving torque, and an actor-critic-based reinforcement learning controller (ACBRLC) is used as an auxiliary controller to output small compensation torque. The improved NMBSMC can be divided into a nominal model-based sliding mode robust controller and a practical model-based integral sliding mode controller. Two sliding mode controllers with different structures make full use of the mathematical model and the measured data of the actual system to improve the vibration equilibrium position tracking accuracy. The ACBRLC uses the tip elastic vibration signal and the prioritized experience replay method to obtain the small reverse compensation torque, which is superimposed with the output of the NMBSMC to suppress tip vibration and improve the positioning accuracy of the hybrid-structured flexible manipulator. Finally, several groups of experiments are designed to verify the effectiveness and robustness of the proposed combined control method.

Recently, laser vision sensors are widely applied in initial point alignment and seam tracking to improve the level of intelligent welding because of good characteristics. However, since the deformation of laser stripe is unobvious at the narrow weld with 0.2 mm width, these methods are not applicable for the narrow weld. Moreover, there are rare researches that could achieve initial point alignment and seam tracking of narrow weld simultaneously. Therefore, an initial point alignment and seam tracking system for narrow weld is proposed in this paper. At first, a laser vision sensor with extra light emitting diode light is used to obtain laser and weld seam image. Besides, the seam feature point is extracted and three-dimensional coordinates can be obtained with vision model. In addition, three controllers including decision controller, initial point alignment controller, and seam-tracking controller are proposed to achieve initial point alignment and seam tracking control in X- and Z-axis directions. Moreover, feature verification, Kalman filter, and output pulse verification are designed to improve the accuracy and stability of this system. Finally, many initial point alignment and seam-tracking experiments of narrow weld are conducted. Experimental results demonstrate that proposed system can well achieve initial point alignment and seam tracking of planar and curved surface narrow weld.