In recent years, with the widespread application of unmanned aerial vehicle technology, frequent unauthorized "black flying" incidents have posed a severe threat to low-altitude airspace security, making the research of counter-UAV detection and perception technologies highly urgent. However, conventional single-sensor detection methods face significant challenges in accuracy and robustness due to the difficulty of capturing micro-targets, dense obstacles, and complex electromagnetic and meteorological disturbances in low-altitude scenarios. To address this issue, this paper provides a comprehensive review of the key algorithms and cutting-edge advancements in low-altitude counter-UAV detection and perception. First, it systematically summarizes the evolution of vision-based object detection algorithms, focusing on the innovative applications of traditional Convolutional Neural Networks, the ultra-lightweight YOLO series, and Transformer architectures with global context perception capabilities in detecting "low, slow, and small" UAVs. Second, it analyzes the detection advantages and technical bottlenecks of non-visual sensors, including radar, acoustic, and radio frequency, when combined with deep learning technologies. Finally, addressing the demands for all-weather detection in complex environments, the paper deeply explores the current development of multimodal fusion algorithms, such as RGB-infrared and multispectral fusion. This paper concludes by summarizing the current challenges in perception robustness and generalization capabilities of core counter-UAV algorithms, and anticipates future critical research directions including visual large models, air-ground multi-agent coordination, and multi-source information fusion, aiming to provide useful references for the technological evolution and engineering practice of low-altitude counter-UAV systems.