High-precision 3D laser scanning pavement data contains rich pavement scene information and certain components associations. Moreover, for pavement maintenance and management, there is an urgent need to develop automatic methods that can extract comprehensive information about different pavement indicators simultaneously. By analyzing the frequency and sparse characteristics of pavement distresses and performance indicators—including the cracks, road markings, rutting, potholes, textures—this paper proposes 3D pavement components decomposition model (3D-PCDM) which decomposes the 3D pavement profiles into sparse components x, low-frequency components f, and vibration components t. Designed high-pass filter was first employed to separate f, then, x and t are separated by total variation de-noising which based on sparse characteristics. Decomposed x can be used to characterize the location and depth information of sparse and sparse derived signals such as cracks, road marks, grooves, and potholes in profiles. Decomposed f can be used to determine the slow deformation of pavement. While decomposed t reflects the fluctuation of the pavement material particles. Experiments were conducted using actual pavement 3D data, the decomposed components can obtain by 3D-PCDM. The effectiveness and accuracy of the x are verified by actual cracks and road markings, the accuracy of extracted sparse components is over 92.75%.

Monitoring marine life through underwater object detection technology serves as a primary means of understanding biodiversity and ecosystem health. However, the complex marine environment, poor resolution, color distortion in underwater optical imaging, and limited computational resources all affect the accuracy and efficiency of underwater object detection. To solve these problems, the YOLOv9s-SD underwater target detection algorithm is proposed to improve the detection performance in underwater environments. We combine the inverted residual structure of MobileNetV2 with Simple Attention Module (SimAM) and Squeeze-and-Excitation Attention (SE) to form the Simple Enhancement attention Module (SME) and optimize AConv, improving the sensitivity of the model to object details. Furthermore, we introduce the lightweight DySample operator to optimize feature recovery, enabling better adaptation to the complex characteristics of underwater targets. Finally, we employ Wise-IoU version 3 (WIoU v3) as the loss function to balance the loss weights for targets of different sizes. In comparison with the YOLOv9s model, according to the experiments conducted on the UPRC and Brackish underwater datasets, YOLOv9s-SD achieves an improvement of 1.3% and 1.2% in the mean Average Precision (mAP), reaching 83.0% and 94.3% on the respective datasets and demonstrating better adaptability to intricate underwater environments.

Establishing a symmetrical model of surrounding vehicles and accurately obtaining the driving state of the surrounding vehicles in the driving environment can improve the safety of driving, which is an important issue that needs to be considered in the automatic driving system or auxiliary driving system. Therefore, we propose an adaptive unscented Kalman filter algorithm based on Interacting Multiple Model (IMM) theory to estimate the state of target vehicle in the high-speed driving environment. To be specific, we use the Constant Turn Rate and Acceleration (CTRA) theory to establish the target vehicle kinematics model, simultaneously, in order to overcome the problem of estimator failure when the yaw rate is close to zero, a simplified version of the CTRA model is also introduced into the estimation process. In addition, the parameter adaptation strategy is added, so the proposed estimator can overcome the uncertainty of the noise model and improve its accuracy. Finally, the effectiveness of proposed state estimation algorithm is verified on the Carsim and Simulink co-simulation platform. The results of simulations and experiments show that the accuracy and stability of IMM-based algorithm is better than the single-model algorithm in different scenarios, and the parameter adaptation strategy brings performance improvement.

This study introduces two innovative methods in the research for use in vision-based tunnel inspection vehicles. First, the image-range stitching method is used to map the sequence images acquired by a camera onto a tunnel layout map. This method reduces the tunnel image-stitching problem to the appropriate parameters, thus solving the problem of mapping equations, ranging from camera pixels to the tunnel layout map. The parameters are obtained using a laser scanner. Secondly, traditional label-based deep learning solely perceives the consistency between pixels and semantically labeled samples, making it challenging to effectively address issues with uncertainty and multiplicity. Consequently, we introduce a method that employs a bidirectional heuristic search approach, utilizing randomly generated seed pixels as hints to locate targets that concurrently appear in both the image and the image semantic generation model. The results reveal the potential for cooperation between laser-scanning and camera-imaging technologies and point out a novel approach of crack detection that appears to be more focused on semantic understanding.