Intelligent　vision　necessitates　the　deployment　of　detectors　that　are　always-on　and　low-power,　mirroring　the　continuous　and　uninterrupted　responsiveness　characteristic　of　human　vision.　Nonetheless,　contemporary　artificial　vision　systems　attain　this　goal　by　the　continuous　processing　of　massive　image　frames　and　executing　intricate　algorithms,　thereby　expending　substantial　computational　power　and　energy.　In　contrast,　biological　data　processing,　based　on　event-triggered　spiking,　has　higher　efficiency　and　lower　energy　consumption.　Here,　this　work　proposes　an　artificial　vision　architecture　consisting　of　spiking　photodetectors　and　artificial　synapses,　closely　mirroring　the　intricacies　of　the　human　visual　system.　Distinct　from　previously　reported　techniques,　the　photodetector　is　self-powered　and　event-triggered,　outputting　light-modulated　spiking　signals　directly,　thereby　fulfilling　the　imperative　for　always-on　with　low-power　consumption.　With　the　spiking　signals　processing　through　the　integrated　synapse　units,　recognition　of　graphics,　gestures,　and　human　action　has　been　implemented,　illustrating　the　potent　image　processing　capabilities　inherent　within　this　architecture.　The　results　prove　the　90%　accuracy　rate　in　human　action　recognition　within　a　mere　five　epochs　utilizing　a　rudimentary　artificial　neural　network.　This　novel　architecture,　grounded　in　spiking　photodetectors,　offers　a　viable　alternative　to　the　extant　models　of　always-on　low-power　artificial　vision　system.　A　spiking-based　artificial　vision　architecture　consisting　of　event-based　spiking　photodetectors　and　artificial　synapses　is　reported.　The　spiking　photodetectors　directly　convert　light　into　electrical　spiking　signals　for　artificial　synapse-based　neuromorphic　computing.　And　the　event-triggered　mechanism　greatly　reduces　the　total　power　consumption.　This　fully　emulated　human　vision　architecture　will　effectively　improve　the　performance　of　artificial　vision　systems.　image