In　natural　scenes,　logos　such　as　trademarks　and　traffic　signs　are　susceptible　to　shooting　angle,　carrier　deformation,　and　scale　changes,　which　reduces　logo　detection　accuracy.　Thus,　this　study　proposes　an　attention　guided　logo　detection　and　recognition　network　(AGLDN)　to　jointly　optimize　the　model　robustness　for　multi-scale　and　complex　deformation.　First,　a　logo　synthesis　dataset　is　established　by　image　collection　and　mask　generation　of　logo　templates,　image　selection　of　logo　background,　and　logo　image　generation.　Then,　based　on　RetinaNet　and　FPN,　multi-scale　features　are　extracted　and　high-level　semantic　feature　mapping　is　formed.　Finally,　the　attention　mechanism　guided　network　is　employed　to　focus　on　the　logo　area,　and　the　influence　of　logo　deformation　on　feature　robustness　is　suppressed　to　improve　logo　detection　and　recognition.　Experimental　results　show　that　the　proposed　method　can　reduce　the　influence　of　scale　changes　and　non-rigid　deformation,　and　improve　detection　accuracy.　©　2024　Chinese　Academy　of　Sciences.　All　rights　reserved.