Abstract: Countermeasures against small unmanned aerial vehicles （UAVs） constitute a pressing issue for safeguarding low-altitude security and national defense construction， in which early warning detection serves as the prerequisite for effective countermeasures. Current counter-small UAV systems employ various detection methods， including radar， radio frequency， acoustics， and electro-optics. Owing to its advantages in facilitating target identification and achieving high tracking and aiming accuracy， electro-optical （EO） detection can be integrated with radar to discern target attributes and autonomously execute the closed loop of search， detection， and tracking. Furthermore， it can function as the targeting subsystem for directed-energy weapons. Therefore， it has become an indispensable and essential method， particularly for countering electromagnetic-silent UAVs that utilize fiber-optic guidance or intelligent autonomous flight based on visual recognition. This paper systematically reviews the technological framework of EO detection for countering small UAVs. First， it elaborates on the fundamental principles underlying the entire EO imaging process， encompassing signal generation， atmospheric transmission， optical focusing， photoelectric conversion， and signal processing. Key evaluation metrics such as the operational range， modulation transfer function， and noise-equivalent temperature difference are clearly defined. Second， a systematic modeling methodology for EO imaging is constructed. Operational range models for both the visible and infrared bands are derived from the perspectives of the energy and frequency domains， respectively， revealing the intrinsic relationship between target characteristics and system performance. Furthermore， this paper focuses on analyzing EO detection and tracking technologies； discusses core challenges such as a low signal-to-noise ratio， small targets， and real-time performance； and delineates the research status and evaluation indicators of mainstream detection and tracking algorithms from traditional methods to deep-learning-based approaches. Finally， the future development of EO detection technology for UAV countermeasures is prospected， and the current situations and research directions of multimodal sensing technologies （such as laser， single-photon， and short-wave infrared detection） as well as multimodal fusion technology are introduced.