Traffic　sign　detection　is　an　integral　part　of　advanced　assisted　driving　systems　and　intelligent　transportation　systems.　Based　on　the　TT100K　dataset,　this　paper　conducts　a　study　on　the　improved　YOLOv5s　traffic　sign　detection　algorithm.　Firstly,　to　address　the　problem　of　low　detection　accuracy　due　to　unbalanced　sample　size　among　different　categories　in　the　TT100K　dataset,　the　replacement　method,　the　paste　method　and　the　conventional　data　expansion　method　were　successively　used　on　the　dataset,　resulting　in　an　increase　in　traffic　sign　detection　accuracy　from　80.9%　to　91.2%.　In　order　to　further　improve　the　application　value　of　the　traffic　sign　detection　algorithm,　this　paper　reduces　the　downsampling　layer　of　the　backbone　in　YOLOv5s　to　obtain　a　detection　head　with　higher　resolution,　thus　getting　the　model　YOLOv5s-strong,　whose　detection　accuracy　is　4.9%　higher　than　YOLOv5s.　Even　surpasses　the　detection　accuracy　and　inference　time　of　YOLOv7　with　less　number　of　parameters　and　calculations　©　2023　IEEE.