In urban pavement crack detection, the single detection platform with visible light sensors is mostly used for detection. Visible light sensors are highly susceptible to lighting conditions. In low-light environments or areas with shadows over the cracks, this may result in false positives or missed detections of pavement cracks. The paper utilizes an image acquisition system that integrates multiple detection platforms, including visible light and infrared sensors, to enhance both the efficiency and accuracy of pavement crack detection. We propose an automatic pavement crack detection approach that uses the similarities and differences between visible light images and infrared image features (MSFSD) to achieve all-weather, real-time detection of pavement cracks. MSFSD mainly includes multi-source image fusion and pixel-level crack detection. In multi-source image fusion, a generative adversarial network (C-GAN) with skip connections and dilated convolutional blocks (CRes2Net blocks) is constructed. The structural similarity index (SSIM) function and the sum of the correlations of differences (SCD) function are used as the loss function of the generator, allowing the fusion image to retain more details and edge information in the source image. In pixel-level crack detection, a crack detection model is constructed, and CRes2Net is used as the core network to obtain pixel-level labels and perform semantic segmentation. It is verified by experiments that MSFSD achieves 85.1% precision and 88.4% recall in different types of crack detection. The MIoU and detection efficiency of MSFSD are 9% and 48% higher than other methods, respectively.

In-pavement crack detection, image segmentation techniques can accurately identify and extract cracks in pavement images with complex backgrounds, however, due to the interference of noise and non-crack damage, the resulting irrelevant non-crack regions reduce the extraction accuracy. To address the above problems, this research presents a road crack segmentation model consisting of a skip connection and decoder of U-Net, residual blocks in the encoding part, and the connected components labeling algorithm (CCL) in the extremity. The proposed method, depth-wise separable convolution replaces the standard convolution to reduce the number of parameters and operation cost. Post-processing using the connected components labeling algorithm solves the interference of irrelevant non-cracked regions. 4710 infrared images are used for the training and testing of the model. The testing results show that the new method for the pavement crack detection in different shadowed images is satisfactory, the detection accuracy can be up to 92.23%, and the algorithm comparison proves that the proposed algorithm is much better than that by the widely used traditional algorithms. In terms of similarity, the Dice Similarity Coefficient (DSC) is improved by 5.97%, 5.22%, and 1.49% compared to FCN, U-Net, and DeeplabV3+ networks, respectively. Therefore, the method is effective in detecting cracks in different pavement IR images, and can significantly improve the accuracy of segmentation results, reduce the error rate, improve the segmentation accuracy and robustness, and can extract cracks in pavement IR images well.

Geomagnetic indoor positioning is an attractive indoor positioning technology due to its infrastructure-free feature. In the matching algorithm for geomagnetic indoor localization, the particle filter has been the most widely used. The algorithm however often suffers filtering divergence when there is continuous variation of the indoor magnetic distribution. The resampling step in the process of implementation would make the situation even worse, which directly lead to the loss of indoor positioning solution. Aiming at this problem, we have proposed an improved particle filter algorithm based on initial positioning error constraint, inspired by the Hausdorff distance measurement point set matching theory. Since the operating range of the particle filter cannot exceed the magnitude of the initial positioning error, it avoids the adverse effect of sampling particles with the same magnetic intensity but away from the target during the iteration process on the positioning system. The effectiveness and reliability of the improved algorithm are verified by experiments.

Quickly and accurately identifying water leakage is one of the important components of the health monitoring of subway tunnels. A mobile vision measurement system consisting of several high-resolution, industrial, charge-coupled device (CCD) cameras is placed on trains to implement structural health monitoring in tunnels. Through the image processing technology proposed in this paper, water leakage areas in subway tunnels can be found and repaired in real time. A lightweight automatic segmentation approach to water leakage using hybrid-supervised-deep-learning technology is proposed. This approach consists of the weakly supervised learning Water Leakage-CAM and fully supervised learning WRDeepLabV3+. The Water Leakage-CAM is used for the automatic labeling of data. The WRDeepLabV3+ is used for the accurate identification of water leakage areas in subway tunnels. Compared with other end-to-end semantic segmentation networks, the hybrid-supervised learning approach can more completely segment the water leakage region when dealing with water leakage in complex environments. The hybrid-supervised-deep-learning approach proposed in this paper achieves the highest MIoU of 82.8% on the experimental dataset, which is 6.4% higher than the second. The efficiency is also 25% higher than the second and significantly outperforms other end-to-end deep learning approaches.

Gesture recognition has always been one of the important in the field of computer vision. The background of dynamic gesture is complex and there are many interference factors. The gesture recognition model based on deep learning usually has high computational cost and poor real-time performance. In addition, deep learning models are limited to recognizing existing categories in the training set and their performance largely depends on the amount of labeled data. To address the above problems, this paper presents a dynamic gesture recognition method named 3SCKI based on three-stream coordinate attention(CA) network, knowledge distillation, and image-text contrastive learning. Specifically, (1)CA is utilized for feature fusion to make the model focus more on target gestures and reduce background interference. (2) The traditional knowledge distillation loss is improved to reduce the amount of calculation and improve the real-time performance. Specifically, the guidance function is added to make the student network only learn the classification probability correctly identified by the teacher network. (3)A multi granularity context prompt template integration method is proposed to construct an improved CLIP visual language model MG-CLIP. Through comparative learning of image features and text features, gesture classification is performed, enabling the model to identify image categories that have not appeared during the training phase. The proposed method is evaluated on the ChaLearn LAP large-scale isolated gesture data set (IsoGD). The results show that our proposed method can obtain recognition rates of 65.87% on the validation set of IsoGD. For single mode data, 3SCKI can obtain the state-of-the-art recognition accuracy on RGB, Depth, and Optical Flow data (61.22%, 58.84%, and 50.30% of the validation set of IsoGD, respectively).