Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent’s attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem of the lack of objective data analysis in the research of table tennis competition, a target detection algorithm-based table tennis trajectory extraction network was proposed to record the trajectory of the table tennis movement in video. The network improved the feature reuse rate in order to achieve a lightweight network and enhance the detection accuracy. The core of the network was the “feature store & return” module, which could store the output of the current network layer and pass the features to the input of the network layer at the next moment to achieve efficient reuse of the features. In this module, the Transformer model was used to secondarily process the features, build the global association information, and enhance the feature richness of the feature map. According to the designed experiments, the detection accuracy of the network was 96.8% for table tennis and 89.1% for target localization. Moreover, the parameter size of the model was only 7.68 MB, and the detection frame rate could reach 634.19 FPS using the hardware for the tests. In summary, the network designed in this paper has the characteristics of both lightweight and high precision in table tennis detection, and the performance of the proposed model significantly outperforms that of the existing models.

SUMMARY: This paper focuses on robust control of a simplest passive model, which is established on a DCLF (discrete control Lyapunov function) -based control system, and presents gait transition method based on the study of purely passive walker. Firstly, the DCLF is introduced to stabilize walking process between steps exponentially by modulating the length of next step. Next, the swing leg trajectory from mid-stance position to foot-strike can be planned. Then the control law is calculated to resist external disturbance. Besides, an impulse is added just before foot-strike to realize a periodic walking pattern on flat or uphill ground. With walking terrain varying, the robot can transit to an adaptive walking gait in a few steps. With different push or pull disturbances acting on hip joint and the robot gait transiting on a continuously slope-changing downhill, the effectiveness of the presented DCLF-based method is verified using simulation experiments. The ability to walk on a changing environment is also presented by simulation results. The insights of this paper can help to develop a robust control method and adaptive walking of dynamic passive locomotion robots.

Recognizing behaviors within classroom settings is vital for gauging educational progress and optimizing teaching methodologies. The complexity of classroom environments often poses challenges for detectors in terms of detection performance. Despite having a simplified structure for analyzing actions in classroom environments, detection transformer (DETR) encounters certain challenges. To overcome these obstacles, we propose a novel real-time end-to-end object detection model called PACR-DETR. Specifically, we introduce an efficient position-aware channel residual network (PACRNet), which excels at extracting information from deep layers along the spatial dimension within complex backgrounds. Notably, it exclusively employs the transformer encoder to process the deepest layer of the feature map. Afterwards, we introduce the efficient residual mixing block (ERM Block), in order to apply convolution operations selectively to a subset of channels. This block underpins the multi-branch perception cross-level fusion module (MPCF Module), which facilitates hierarchical and cross-layer information fusion. Through a bidirectional chain, the multi-branch perception cross-level fusion network (MPCFNet) is architected to comprehensively integrate multi-scale gradient information. Finally, we integrate the normalized Wasserstein distance (NWD) with the Inner-MPDIoU loss function to enhance the recognition performance for small-scale objects. The results show that the PACR-DETR outperforms other methods in detection performance and real-time capability. On the SCB03-S dataset for classroom behavior detection against complex backgrounds, the proposed method attains a precision of 72.1%, outperforming YOLOv6 by 14.1% and YOLOv8m by 4.5%. Furthermore, it reaches 41.67 FPS, meeting the requirements for real-time application.

To address the challenges associated with supervising workers who wear safety belts while working at heights, this study proposes a solution involving the utilization of an object detection model to replace manual supervision. A novel object detection model, named ESE-YOLOv8, is introduced. The integration of the Efficient Multi-Scale Attention (EMA) mechanism within this model enhances information entropy through cross-channel interaction and encodes spatial information into the channels, thereby enabling the model to obtain rich and significant information during feature extraction. By employing GSConv to reconstruct the neck into a slim-neck configuration, the computational load of the neck is reduced without the loss of information entropy, allowing the attention mechanism to function more effectively, thereby improving accuracy. During the model training phase, a regression loss function named the Efficient Intersection over Union (EIoU) is employed to further refine the model’s object localization capabilities. Experimental results demonstrate that the ESE-YOLOv8 model achieves an average precision of 92.7% at an IoU threshold of 50% and an average precision of 75.7% within the IoU threshold range of 50% to 95%. These results surpass the performance of the baseline model, the widely utilized YOLOv5 and demonstrate competitiveness among state-of-the-art models. Ablation experiments further confirm the effectiveness of the model’s enhancements.