In　this　paper,　a　systematic　review　of　aero-engine　defect　detection　methods　is　presented,　encompassing　the　general　procedure,　traditional　and　intelligent　detection　algorithms,　performance　optimization,　and　future　trends.　The　complete　process　and　innovative　theories　of　aero-engine　visual　defect　detection　are　analyzed　in　this　overview.　Specifically,　a　five-level　taxonomy　is　designed,　with　each　level　further　subdivided　to　provide　deeper　insights,　from　data　acquisition　and　task-oriented　detection　with　nondestructive　testing　(NDT),　to　practical　applications.　By　leveraging　multiscale　feature　fusion-based　detection,　these　methods　achieve　enhanced　precision　in　identifying　defects　across　varying　scales　and　complexities.　Moreover,　in-depth　discussions　and　outlooks　on　performance　optimization　and　efficient　deployment　strategies　are　provided　to　promote　advanced　intelligent　maintenance　solutions　for　high-end　equipment,　which　may　encourage　more　multidisciplinary　collaborations.　Compared　to　other　existing　surveys,　this　work　comprehensively　outlines　how　computer　vision　(CV)-based　methods　can　assist　in　aero-engine　defect　detection　for　intelligent　decision-making,　and　a　connection　between　NDT　technology　and　CV-based　inspection　has　been　established,　thereby　drawing　greater　attention　to　the　application　of　artificial　intelligence　to　further　enhance　the　development　of　industrial　predictive　maintenance.