In this paper, an improved Transformer network is proposed, which reduces the overall computation of the network by nearly 50°/o while still maintaining good network performance while only retaining K and V values. Experiments show that with Swin Transformer as the backbone network, the improved method proposed in this paper can reduce the training time and testing time while maintaining high accuracy.

In this paper, we propose a lightweight mask detection algorithm and implement an intelligent vehicle system. The algorithm uses YOLOv5s as the backbone network, and at the same time incorporates the SE attention mechanism to optimize the timeliness, and is finally deployed on an intelligent vehicle system with BCM2711 as the control platform. Experiments prove that the algorithm proposed in this paper reduces the detection time by 30% while ensuring a higher MAP, which has certain value for promotion.

Camera and inertial measurement unit (IMU) based three-dimensional (3D) human pose estimation flourished in the last decade. However, existing methods are difficult to apply to clinical environments because of the many required devices. In this study, we attempt to reduce the devices and maintain their robustness. Occ-Corrector, a semantic convolution-based neural network, was proposed to estimate 3D human poses robustly in occlusion cases involving a single camera with the help of a few IMUs. It includes a simple Sensor-Reshape, which helps to fuse IMU information with the camera more effectively, and a new strategy of alternating the loss function, allowing the model to improve its accuracy in predicting challenging poses. By conducting an inverse analysis of the weight matrix, the importance of each IMU was investigated for device simplification purposes. The proposed method was verified by using the Total Capture dataset with two hypothetical occlusion conditions, and the result showed that only five IMUs can make the accuracy stable in different occlusion situations.