Liu Zhiguo, Zhang Enzheng, Ding Qian, Liao Weijie, Wu Zixiang
School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Sensors (Basel). 2024 Dec 26;25(1):85. doi: 10.3390/s25010085.
Accurate detection and tracking of dynamic objects are critical for enabling skill demonstration and effective skill generalization in robotic skill learning and application scenarios. To further improve the detection accuracy and tracking speed of the YOLOv8s model in dynamic object tracking tasks, this paper proposes a method to enhance both detection precision and speed based on YOLOv8s architecture. Specifically, a Focused Linear Attention mechanism is introduced into the YOLOv8s backbone network to enhance dynamic object detection accuracy, while the Ghost module is incorporated into the neck network to improve the model's tracking speed for dynamic objects. By mapping the motion of dynamic objects across frames, the proposed method achieves accurate trajectory tracking. This paper provides a detailed explanation of the improvements made to YOLOv8s for enhancing detection accuracy and speed in dynamic object detection tasks. Comparative experiments on the MS-COCO dataset and the custom dataset demonstrate that the proposed method has a clear advantage in terms of detection accuracy and processing speed. The dynamic object detection experiments further validate the effectiveness of the proposed method for detecting and tracking objects at different speeds. The proposed method offers a valuable reference for the field of dynamic object detection, providing actionable insights for applications such as robotic skill learning, generalization, and artificial intelligence-driven robotics.
在机器人技能学习和应用场景中，准确检测和跟踪动态物体对于实现技能演示和有效的技能泛化至关重要。为了在动态物体跟踪任务中进一步提高YOLOv8s模型的检测精度和跟踪速度，本文提出了一种基于YOLOv8s架构提高检测精度和速度的方法。具体而言，将聚焦线性注意力机制引入YOLOv8s主干网络以提高动态物体检测精度，同时将Ghost模块并入颈部网络以提高模型对动态物体的跟踪速度。通过跨帧映射动态物体的运动，该方法实现了精确的轨迹跟踪。本文详细解释了对YOLOv8s进行改进以提高动态物体检测任务中的检测精度和速度的方法。在MS-COCO数据集和自定义数据集上的对比实验表明，该方法在检测精度和处理速度方面具有明显优势。动态物体检测实验进一步验证了该方法在检测和跟踪不同速度物体方面的有效性。该方法为动态物体检测领域提供了有价值的参考，为机器人技能学习、泛化以及人工智能驱动的机器人等应用提供了可操作的见解。
