Abstract: An anti-crash helicopter seat integrating multiple protections, including a main framework, a weighing sensor, a magnetorheological damper system, an upper body forced tensioning and quick disengagement system, a seat pan, a multifunctional vest and a controller, wherein the seat pan is slidably connected with the main framework in a vertical direction, the magnetorheological damper system is fixedly arranged on the seat pan and is connected with the main framework by the weighing sensor, the upper body forced tensioning and quick disengagement system is arranged on the seat pan, the multifunctional vest is connected with the seat pan by the upper body forced tensioning and quick disengagement system, and the controller is connected with the weighing sensor and the magnetorheological damper system respectively.

Abstract: The invention discloses a Transformer-based multi-scale pedestrian re-identification method. The present invention proposes a pedestrian re-identification network based on multi-scale pedestrian feature extraction and Transformer. Firstly, we designed a multi-scale feature cascade module, which aims to mine detailed feature information of pedestrians at different depths and scales, so as to obtain stronger feature representation. Secondly, we constructed a feature extraction based on Transformer to learn pedestrian features at a global scale. Finally, the features output by the Transformer are aggregated to obtain a better expression of pedestrian features, thereby improving the discrimination ability of the model. The result shows that this method has better robustness and adaptive ability, and effectively enhances the generalization ability of the model.

Abstract: The present application relates to an occluded pedestrian re-identification method, including steps of obtaining global features and local features of occluded pedestrians, and recombining the local features into a local feature map; obtaining a heat map of key-points of pedestrian images and a group of key-point confidences, obtaining a group of features of the pedestrian key-points by using the local feature map and the heat map; obtaining a local feature group by using the global features to enhance each key-point feature in the group of features of pedestrian key-points according to Conv, and an adjacency matrix of key-points is obtained through the key-points, the local feature group and the adjacency matrix of key-points are used as the input of GCN to obtain the final features of pedestrian key-points.