Infrared　small　target　detection　(IRSTD)　plays　a　vital　role　in　various　fields,　especially　in　military　early　warning　and　maritime　rescue.　Its　main　goal　is　to　accurately　locate　targets　at　long　distances.　Current　deep　learning　(DL)-based　methods　mainly　rely　on　mask-to-mask　or　box-to-box　regression　training　approaches,　making　considerable　progress　in　detection　accuracy.　However,　these　methods　rely　on　large　amounts　of　training　data　with　expensive　manual　annotation.　Although　some　researchers　attempt　to　reduce　the　cost　using　single-point　weak　supervision　(SPWS),　the　limited　labeling　accuracy　significantly　degrades　the　detection　performance.　To　address　these　issues,　we　propose　a　novel　point-to-point　regression　high-resolution　dynamic　network　(P2P-HDNet),　which　can　accurately　locate　the　target　center　using　only　single-point　annotation.　Specifically,　we　first　devise　the　high-resolution　cross-feature　extraction　module　(HCEM)　to　provide　richer　target　detail　information　for　the　deep　feature　maps.　Notably,　HCEM　maintains　high　resolution　throughout　the　feature　extraction　process　to　minimize　information　loss.　Then,　the　dynamic　coordinate　fusion　module　(DCFM)　is　devised　to　fully　fuse　the　multidimensional　features　and　enhance　the　positional　sensitivity.　Finally,　we　devise　an　adaptive　target　localization　detection　head　(ATLDH)　to　further　suppress　clutter　and　improve　the　localization　accuracy　by　regressing　the　Gaussian　heatmap　and　adaptive　nonmaximal　suppression　strategy.　Extensive　experimental　results　show　that　P2P-HDNet　can　achieve　better　detection　accuracy　than　the　state-of-the-art　(SOTA)　methods　with　only　single-point　annotation.　©　1980-2012　IEEE.