Bridge crack detection is essential to prevent transportation accidents. However, the surrounding environment has great interference with the detection of cracks, which makes it difficult to ensure the accuracy of the detection. In order to accurately detect bridge cracks, we proposed an end-to-end model named Skip-Squeeze-and-Excitation Networks (SSENets). It is mainly composed of the Skip-Squeeze-Excitation (SSE) module and the Atrous Spatial Pyramid Pooling (ASPP) module. The SSE module uses skip-connection strategy to enhance the gradient correlation between the shallow network and deeper network, alleviating the vanishing gradient caused by the deepening of the network. The ASPP module can extract multi-scale contextual information of images, while the depthwise separable convolution reduces computational complexity. In order to avoid destroying the topology of crack, we used atrous convolution instead of the pooling layer. The proposed SSENets achieved a detection accuracy of 97.77%, which performed better than the models we compared it with. The designed SSE module which used skip-connection strategy can be embedded in other convolutional neural networks (CNNs) to improve their performance.

Cracks are one of the most serious defects that threaten the safety of bridges. In order to detect different forms of cracks in different collection environments quickly and accurately, we proposed a pixel-level crack segmentation network based on convolutional neural networks, which is called the Skip Connected Crack Detection Network (SCCDNet). The network is composed of three parts: the Encoder module with 13 convolutional layers pretrained in the VGG-16 network, the Decoder module with a densely connected structure, and the Skip-Squeeze-and-Excitation (SSE) module which connects the feature map shaving the same resolution in the Encoder and Decoder. We used depthwise separable convolution to improve the accuracy of crack segmentation while reducing the complexity of the model. In this paper, a dataset containing cracks collected in different scenes was established, and SCCDNet was trained and tested on this dataset. Compared with the advanced models, SCCDNet obtained the best crack segmentation performance, while F-score reached 0.7763.

ICP is a well-known method for point cloud registration but it only uses geometric information to do this, which will result in bad results in some similar structures. Adding color information when registering will improve the performance. However, color information of point cloud, such as gray, varies differently under different lighting conditions. Using gray as the color information to register can cause large errors and even wrong results. To solve this problem, we propose a color point cloud registration algorithm based on hue, which has good robustness at different lighting conditions. We extract the hue component according to the color information of point clouds and make the hue distribution of the tangent plane continuous. The error function consists of color and geometric error of two point clouds under the current transformation. We optimize the error function using the Gauss–Newton method. If the value of the error function is less than the preset threshold or the maximum number of iterations is reached, the current transformation relationship is required. We use RGB-D Scenes V2 dataset to evaluate our algorithm and the results show that the average recall of our algorithms is 8.63% higher than that of some excellent algorithms, and its RMSE of 14.3% is lower than that of the other compared algorithms.

Semantic scene understanding is a fundamental capability for autonomous vehicles. Under challenging lighting conditions, such as nighttime and on-coming headlights, the semantic scene understanding performance using only RGB images are usually degraded. Thermal images can provide complementary information to RGB images, so many recent semantic segmentation networks have been proposed using RGB-Thermal (RGB-T) images. However, most existing networks focus only on improving segmentation accuracy for single image frames, omitting the information consistency between consecutive frames. To provide a solution to this issue, we propose a temporal-consistent framework for RGB-T semantic segmentation, which introduces a virtual view image generation module to synthesize a virtual image for the next moment, and a consistency loss function to ensure the segmentation consistency. We also propose an evaluation metric to measure both the accuracy and consistency for semantic segmentation. Experimental results show that our framework outperforms state-of-the-art methods.

Semantic scene understanding is an important capability for autonomous vehicles. Despite recent advances in RGB-Thermal (RGB-T) semantic segmentation, existing methods often rely on parameter-heavy models, which are particularly constrained by the lack of precisely-labeled training data. To alleviate this limitation, we propose a data-driven method, SyntheticSeg, to enhance RGB-T semantic segmentation. Specifically, we utilize generative models to generate synthetic RGB-T images from the semantic layouts in real datasets and construct a large-scale, high-fidelity synthetic dataset to provide the segmentation models with sufficient training data. We also introduce a novel metric that measures both the scarcity and segmentation difficulty of semantic layouts, guiding sampling from the synthetic dataset to alleviate class imbalance and improve the overall segmentation performance. Experimental results on a public dataset demonstrate our superior performance over the state of the arts.