This　paper　established　three　deep　residual　neural　network　models　with　different　architectures　for　traffic　sign　detection.　Also,　a　new　systematic　analytic　hierarchy　process　method　for　model　performance　evaluation　has　been　proposed,　which　was　utilized　to　determine　the　configuration　of　the　deep　learning　model.　In　this　paper,　four　evaluation　metrics　were　used　for　analytic　hierarchy　process　measurement,　they　are　accuracy,　stability,　response　time,　and　system　capability.　Based　on　the　Tsinghua-Tencent　100K　dataset,　experimental　results　verified　the　feasibility　of　the　proposed　models　for　traffic　sign　detection　and　recognition　which　has　training　and　testing　accuracy　of　99.03%　and　98.01%　respectively.　©　The　2023　International　Conference　on　Artificial　Life　and　Robotics　(ICAROB2023).