The perception of the environment around a land vehicle plays a crucial role for its driving assistant system. Knowledge of the road terrain is useful for handling its characteristics while driving the vehicles and improving passengers' safety and comfort. In this article, an approach to classifying road-terrain vehicles is presented. An accelerometer is mounted on the suspension of the vehicle to measure the vibration that represents the characteristics of the road terrain, and the road profile can be calculated by knowing the speed and one-quarter-dynamic model of the vehicle. The optimized classifier and features, speed independency, and the effect of employing principal component analysis (PCA) are investigated, and the simulation shows that this acceleration-based approach is feasible for land vehicles in a range of outdoor scenarios.

In complex environments with dense pedestrian traffic, mobile robots often experience errors and instability during trajectory tracking and dynamic obstacle avoidance tasks. This paper presents a scene perception and decision-making strategy combined with deep reinforcement learning. Temporal sequences of LiDAR data and sub-goal were used as input, and action output is generated via an end-to-end network. We designed an adaptive heading reward that guides the robot to proactively avoid pedestrians while efficiently moving toward its target. Through continuous interaction with a dynamic environment, the robot learns an optimal decision-making strategy by maximizing cumulative rewards. A series of simulation experiments and real-world validations demonstrate that the proposed strategy achieves an effective balance between collision avoidance and real-time performance in robotic navigation. Furthermore, extensive results confirm that the method remains robust in unfamiliar environments and in varying crowd densities. Finally, tests on a hardware platform indicate that the strategy offers strong stability and adaptability in practical applications, effectively meeting obstacle avoidance requirements and validating its reliability in complex dynamic settings.

Recently, synthetic aperture radar (SAR) ship detection is used in many applications within the marine field, such as fishery management, traffic control, and urgent rescue operations. Meanwhile, deep learning-based methods have bought new capabilities for ship detection in SAR images on account of high accuracy and robustness. However, several challenges remain to be addressed: 1) the shapes of the ships in SAR images have a relatively extreme aspect ratio comparing to the target objects in the optical images, and 2) complex background and clutter noise result in adverse effects for the network to extract prototypical SAR target features, which limit the ship detection performance. To address these issues, this paper proposes two effective approaches to augment the feature extraction ability of the network. Firstly, IOU (Intersection over Union) K-means is carried out to settle the extreme aspect ratio problem. The IOU K-means, as a preprocessing step, clusters a set of aspect ratios from datasets that are suitable for ship detection. Secondly, we embed a soft thresholding attention module (STA) in the network to suppress the impact of noise and complex background. The comparison results with several state-of-the-art object detection algorithms confirm the efficiency and feasibility of proposed approaches.

The presence of fake pictures affects the reliability of visible face images under specific circumstances. This paper presents a novel adversarial neural network designed named as the FTSGAN for infrared and visible image fusion and we utilize FTSGAN model to fuse the face image features of infrared and visible image to improve the effect of face recognition. In FTSGAN model design, the Frobenius norm (F), total variation norm (TV), and structural similarity index measure (SSIM) are employed. The F and TV are used to limit the gray level and the gradient of the image, while the SSIM is used to limit the image structure. The FTSGAN fuses infrared and visible face images that contains bio-information for heterogeneous face recognition tasks. Experiments based on the FTSGAN using hundreds of face images demonstrate its excellent performance. The principal component analysis (PCA) and linear discrimination analysis (LDA) are involved in face recognition. The face recognition performance after fusion improved by 1.9% compared to that before fusion, and the final face recognition rate was 94.4%. This proposed method has better quality, faster rate, and is more robust than the methods that only use visible images for face recognition.