Background:　In　the　context　of　rapid　urbanization,　the　need　for　building　safety　and　durability　assessment　is　becoming　increasingly　prominent.　Objective:　The　aim　of　this　paper　is　to　review　the　strengths　and　weaknesses　of　the　main　non-destructive　testing　(NDT)　techniques　in　construction　engineering,　with　a　focus　on　the　application　of　deep　learning　in　image-based　NDT.　Design:　We　surveyed　more　than　80　papers　published　within　the　last　decade　to　assess　the　role　of　deep　learning　techniques　combined　with　NDT　in　automated　inspection　in　construction.　Results:　Deep　learning　significantly　enhances　defect　detection　accuracy　and　efficiency　in　construction　NDT,　particularly　in　image-based　techniques　such　as　infrared　thermography,　ground-penetrating　radar,　and　ultrasonic　inspection.　Multi-technology　fusion　and　data　integration　effectively　address　the　limitations　of　single　methods.　However,　challenges　remain,　including　data　complexity,　resolution　limitations,　and　insufficient　sample　sizes　in　NDT　images,　which　hinder　deep　learning　model　training　and　optimization.　Conclusions:　This　paper　not　only　summarizes　the　existing　research　results,　but　also　discusses　the　future　optimization　direction　of　the　target　detection　network　for　NDT　defect　data,　aiming　to　promote　intelligent　development　in　the　field　of　non-destructive　testing　of　buildings,　and　to　provide　more　efficient　and　accurate　solutions　for　building　maintenance.