Rapid　advancement　of　intelligent　transportation　systems　(ITS)　and　autonomous　driving　(AD)　have　shown　the　importance　of　accurate　and　efficient　detection　of　traffic　signs.　However,　certain　drawbacks,　such　as　balancing　accuracy　and　real-time　performance,　hinder　the　deployment　of　traffic　sign　detection　algorithms　in　ITS　and　AD　domains.　In　this　study,　a　novel　traffic　sign　detection　algorithm　was　proposed　based　on　the　bidirectional　Res2Net　architecture　to　achieve　an　improved　balance　between　accuracy　and　speed.　An　enhanced　backbone　network　module,　called　C2Net,　which　uses　an　upgraded　bidirectional　Res2Net,　was　introduced　to　mitigate　information　loss　in　the　feature　extraction　process　and　to　achieve　information　complementarity.　Furthermore,　a　squeeze-and-excitation　attention　mechanism　was　incorporated　within　the　channel　attention　of　the　architecture　to　perform　channel-level　feature　correction　on　the　input　feature　map,　which　effectively　retains　valuable　features　while　removing　non-essential　features.　A　series　of　ablation　experiments　were　conducted　to　validate　the　efficacy　of　the　proposed　methodology.　The　performance　was　evaluated　using　two　distinct　datasets:　the　Tsinghua-Tencent　100K　and　the　CSUST　Chinese　traffic　sign　detection　benchmark　2021.　On　the　TT100K　dataset,　the　method　achieves　precision,　recall,　and　Map0.5　scores　of　83.3%,　79.3%,　and　84.2%,　respectively.　Similarly,　on　the　CCTSDB　2021　dataset,　the　method　achieves　precision,　recall,　and　Map0.5　scores　of　91.49%,　73.79%,　and　81.03%,　respectively.　Experimental　results　revealed　that　the　proposed　method　had　superior　performance　compared　to　conventional　models,　which　includes　the　faster　region-based　convolutional　neural　network,　single　shot　multibox　detector,　and　you　only　look　once　version　5.　©　2023　Tech　Science　Press.　All　rights　reserved.