Extrinsic parameter calibration is the foundation and prerequisite for LiDAR and camera data fusion of the autonomous system. This technology is widely used in fields such as autonomous driving, mobile robots, intelligent surveillance, and visual measurement. The learning-based method is one of the targetless calibrating methods in LiDAR and camera calibration. Due to its advantages of fast speed, high accuracy, and robustness under complex conditions, it has gradually been applied in practice from a simple theoretical model in just a few years, becoming an indispensable and important method. This paper systematically summarizes the research and development of this type of method in recent years. According to the principle of calibration parameter estimation, learning-based calibration algorithms are divided into two categories: accurate calibrating estimation and relative calibrating prediction. The evolution routes and algorithm frameworks of these two types of algorithms are elaborated, and the methods used in the algorithms’ steps are summarized. The algorithm mechanism, advantages, limitations, and applicable scenarios are discussed. Finally, we make a summary, pointing out existing research issues and trends for future development.

With the rapid development of terahertz radar technology, the electromagnetic device for terahertz radar cross-section (RCS) reduction is worth investigating. However, the existing research concentrates on the RCS reduction metasurface with fixed performance working in the microwave band. This paper proposes a terahertz dynamic coding metasurface integrated with vanadium dioxide (VO₂) for dual-polarized, dual-band, and wide-angle RCS reduction. The simulation result indicates that by switching the state of the VO₂ between insulator and metal, the metasurface can realize the effective RCS reduction at 0.18 THz to 0.24 THz and 0.21 THz to 0.39 THz under the left-handed and right-handed circularly polarized incident waves. When the polar and azimuth angles of the incident wave vary from 0° to 40° and 0° to 360° respectively, this metasurface can maintain a 10 dB RCS reduction. This work has potential value in the terahertz stealth field.

Object detection is an essential component of autonomous driving, unmanned aerial vehicle (UAV) reconnaissance, and other domains. It equips drones and vehicles with the capability to perceive and comprehend their surrounding environment, making it a crucial technology for achieving safe and reliable autonomous driving as well as UAV spot reconnaissance. This paper proposes an end-to-end, high-precision, multi-scale real-time detection algorithm called MRT-YOLO based on YOLOv8. Firstly, in the feature downsampling process of the backbone network, we extend the channel depth to enhance the model’s learning capability for fine-grained features and thereby improve its performance in retaining feature information. Secondly, we enhance the cross-stage partial layer version 2 (C2f) module in YOLOv8 by incorporating a channel self-attention mechanism within it, which optimizes performance through effective feature interaction and integration. Simultaneously, we also employ an improved bidirectional feature pyramid network (BiFPN) and introduce the proposed multi-scale feature learning (MFL) module to further enhance the model’s feature extraction ability. In this study, we fuse the feature maps (C2, C3, C4, and C5) from the backbone network to generate a new feature map C6, thus increasing cross-connections between low-level and high-level features. Lastly, a multi-scale small object detection structure is designed to enhance recognition sensitivity toward densely distributed small objects. The proposed algorithm’s effectiveness and superiority are demonstrated through experiments conducted on two datasets: VisDrone (UAV vision dataset) and BDD100K (automatic driving dataset).