Abstract
In modern complex environments, achieving accurate and efficient target localization is essential in numerous fields. However, existing systems often face limitations in both accuracy and the ability to recognize small targets. In this study, we propose a bionic stabilized localization system based on CA-YOLO, designed to enhance both target localization accuracy and small target recognition capabilities. Acting as the "brain" of the system, the target detection algorithm emulates the visual focusing mechanism of animals by integrating bionic modules into the YOLO backbone network. These modules include the introduction of a small target detection head and the development of a Characteristic Fusion Attention Mechanism (CFAM). Furthermore, drawing inspiration from the human Vestibulo-Ocular Reflex (VOR), a bionic pan-tilt tracking control strategy is developed, which incorporates central positioning, stability optimization, adaptive control coefficient adjustment, and an intelligent recapture function. The experimental results show that CA-YOLO outperforms the original model on standard datasets (COCO and VisDrone), with average accuracy metrics improved by 3.94%and 4.90%, this http URL time-sensitive target localization experiments validate the effectiveness and practicality of this bionic stabilized localization system.
Abstract (translated)
在现代复杂环境中，实现准确高效的定位目标对于众多领域而言至关重要。然而，现有的系统通常面临精度和识别小目标能力上的局限性。本研究提出了一种基于CA-YOLO的仿生稳定定位系统，旨在提升目标定位精度以及对小目标的识别能力。作为系统的“大脑”，目标检测算法通过将仿生模块整合进YOLO骨干网络中来模拟动物视觉聚焦机制。这些模块包括引入小目标检测头部和开发特征融合注意机制（CFAM）。此外，该研究从人类前庭眼动反射（VOR）中获得灵感，发展了一种仿生云台跟踪控制策略，集成了中心定位、稳定性优化、自适应控制系数调整以及智能重捕功能。实验结果表明，在标准数据集(COCO和VisDrone)上，CA-YOLO的表现优于原始模型，平均精度指标分别提高了3.94%和4.90%，这证明了该仿生稳定定位系统在时间敏感的目标定位试验中的有效性和实用性。
URL
https://arxiv.org/abs/2602.07523
