PDF(Pubmed)
Abstract：
UNASSIGNED: Sea jellyfish stings pose a threat to human health, and traditional detection methods face challenges in terms of accuracy and real-time capabilities.
UNASSIGNED: To address this, we propose a novel algorithm that integrates YOLOv4 object detection, an attention mechanism, and PID control. We enhance YOLOv4 to improve the accuracy and real-time performance of detection. Additionally, we introduce an attention mechanism to automatically focus on critical areas of sea jellyfish stings, enhancing detection precision. Ultimately, utilizing the PID control algorithm, we achieve adaptive adjustments in the robot\'s movements and posture based on the detection results. Extensive experimental evaluations using a real sea jellyfish sting image dataset demonstrate significant improvements in accuracy and real-time performance using our proposed algorithm. Compared to traditional methods, our algorithm more accurately detects sea jellyfish stings and dynamically adjusts the robot\'s actions in real-time, maximizing protection for human health.
UNASSIGNED: The significance of this research lies in providing an efficient and accurate sea jellyfish sting detection algorithm for intelligent robot systems. The algorithm exhibits notable improvements in real-time capabilities and precision, aiding robot systems in better identifying and addressing sea jellyfish stings, thereby safeguarding human health. Moreover, the algorithm possesses a certain level of generality and can be applied to other applications in target detection and adaptive control, offering broad prospects for diverse applications.
UNASSIGNED: To address this, we propose a novel algorithm that integrates YOLOv4 object detection, an attention mechanism, and PID control. We enhance YOLOv4 to improve the accuracy and real-time performance of detection. Additionally, we introduce an attention mechanism to automatically focus on critical areas of sea jellyfish stings, enhancing detection precision. Ultimately, utilizing the PID control algorithm, we achieve adaptive adjustments in the robot\'s movements and posture based on the detection results. Extensive experimental evaluations using a real sea jellyfish sting image dataset demonstrate significant improvements in accuracy and real-time performance using our proposed algorithm. Compared to traditional methods, our algorithm more accurately detects sea jellyfish stings and dynamically adjusts the robot\'s actions in real-time, maximizing protection for human health.
UNASSIGNED: The significance of this research lies in providing an efficient and accurate sea jellyfish sting detection algorithm for intelligent robot systems. The algorithm exhibits notable improvements in real-time capabilities and precision, aiding robot systems in better identifying and addressing sea jellyfish stings, thereby safeguarding human health. Moreover, the algorithm possesses a certain level of generality and can be applied to other applications in target detection and adaptive control, offering broad prospects for diverse applications.
摘要：
■海水母叮咬对人体健康构成威胁,传统的检测方法在准确性和实时性方面面临挑战。
■为了解决这个问题，我们提出了一种集成YOLOv4对象检测的新算法，一种注意力机制，和PID控制。我们增强了YOLOv4，以提高检测的准确性和实时性。此外,我们引入了一种关注机制，自动关注海母叮咬的关键区域，提高检测精度。最终,利用PID控制算法，根据检测结果实现机器人运动和姿态的自适应调整。使用真实的海水母刺痛图像数据集进行的大量实验评估表明，使用我们提出的算法可以显着提高准确性和实时性。与传统方法相比，我们的算法更准确地检测海水母叮咬，并实时动态调整机器人的动作，最大限度地保护人类健康。
■这项研究的意义在于为智能机器人系统提供一种高效，准确的海水母叮咬检测算法。该算法在实时能力和精度方面表现出显著的改进，帮助机器人系统更好地识别和解决海母叮咬，从而维护人类健康。此外，该算法具有一定的通用性,可以应用于目标检测和自适应控制中的其他应用,为各种应用提供了广阔的前景。
■为了解决这个问题，我们提出了一种集成YOLOv4对象检测的新算法，一种注意力机制，和PID控制。我们增强了YOLOv4，以提高检测的准确性和实时性。此外,我们引入了一种关注机制，自动关注海母叮咬的关键区域，提高检测精度。最终,利用PID控制算法，根据检测结果实现机器人运动和姿态的自适应调整。使用真实的海水母刺痛图像数据集进行的大量实验评估表明，使用我们提出的算法可以显着提高准确性和实时性。与传统方法相比，我们的算法更准确地检测海水母叮咬，并实时动态调整机器人的动作，最大限度地保护人类健康。
■这项研究的意义在于为智能机器人系统提供一种高效，准确的海水母叮咬检测算法。该算法在实时能力和精度方面表现出显著的改进，帮助机器人系统更好地识别和解决海母叮咬，从而维护人类健康。此外，该算法具有一定的通用性,可以应用于目标检测和自适应控制中的其他应用,为各种应用提供了广阔的前景。
