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%, respectively.Further time-sensitive target localization experiments validate the effectiveness and practicality of this bionic stabilized localization system.

翻译：在现代复杂环境中，实现精准高效的目标定位在众多领域至关重要。然而，现有系统通常在定位精度与小目标识别能力方面均存在局限。本研究提出一种基于CA-YOLO的仿生稳定定位系统，旨在同时提升目标定位精度与小目标识别能力。作为系统的"大脑"，目标检测算法通过在YOLO骨干网络中集成仿生模块，模拟动物的视觉聚焦机制。这些模块包括引入小目标检测头以及构建特征融合注意力机制（CFAM）。此外，受人体前庭眼反射（VOR）启发，本研究开发了仿生云台跟踪控制策略，该策略融合了中心定位、稳定性优化、自适应控制系数调整及智能重捕获功能。实验结果表明，CA-YOLO在标准数据集（COCO与VisDrone）上均优于原始模型，平均精度指标分别提升3.94%与4.90%。进一步的时效敏感目标定位实验验证了该仿生稳定定位系统的有效性与实用性。