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This paper explores the development of elastic LiDAR technology, focusing specifically on key components relevant to solid target scanning applications. By analyzing its fundamentals and working mechanisms, the advantages of elastic LiDAR for precise measurement and environmental sensing are demonstrated. This paper emphasizes innovative advances in emitters and scanning systems, and examines the impact of optical design on performance and cost. Various ranging methods are discussed. Practical application cases of elastic LiDAR are presented, and future trends and challenges are explored. The purpose of this paper is to provide a comprehensive perspective on the technical details of elastic LiDAR, the current state of application, and future directions. All instances of "LiDAR" in this paper specifically refer to elastic LiDAR. [ABSTRACT FROM AUTHOR]

In recent years, the safety issues of high-speed railways have remained severe. The intrusion of personnel or obstacles into the perimeter has often occurred in the past, causing derailment or parking, especially in the case of bad weather such as fog, haze, rain, etc. According to previous research, it is difficult for a single sensor to meet the application needs of all scenario, all weather, and all time domains. Due to the complementary advantages of multi-sensor data such as images and point clouds, multi-sensor fusion detection technology for high-speed railway perimeter intrusion is becoming a research hotspot. To the best of our knowledge, there has been no review of research on multi-sensor fusion detection technology for high-speed railway perimeter intrusion. To make up for this deficiency and stimulate future research, this article first analyzes the situation of high-speed railway technical defense measures and summarizes the research status of single sensor detection. Secondly, based on the analysis of typical intrusion scenarios in high-speed railways, we introduce the research status of multi-sensor data fusion detection algorithms and data. Then, we discuss risk assessment of railway safety. Finally, the trends and challenges of multi-sensor fusion detection algorithms in the railway field are discussed. This provides effective theoretical support and technical guidance for high-speed rail perimeter intrusion monitoring. [ABSTRACT FROM AUTHOR]

In view of the poor recognition results of long-distance intrusion targets in the research of high-speed railway perimeter intrusion detection, a high-speed railway perimeter intrusion detection method based on improved YOLOv5 is proposed. Firstly, the coordinate attention mechanism is integrated in the neck network of YOLOv5s model to improve the feature extraction capability of the algorithm. Secondly, a prediction layer is added to improve the detection performance of long-distance intruders. Further, the K-means clustering algorithm is used to obtain the appropriate anchors of the dataset. Finally, the bounding box regression loss function is improved to increase the positioning accuracy of the bounding box. In addition, a high-speed railway perimeter intrusion image sample library for typical scenes is constructed, and the improved model is trained and tested by using the sample library. The experimental results show that the improved model can effectively detect the intruders into the perimeter of high-speed railway. The improved algorithm improves the mean average precision (IOU =0.5) by 5.22% over the original YOLOv5s. The model has strong generalization ability in small target scenes. [ABSTRACT FROM AUTHOR]