Polarization　imaging　technology　can　be　applied　to　unveil　the　interaction　between　light　and　matter　by　harnessing　the　transverse　vector　wave　attributes　of　light,　thus　to　accentuate　target　characteristics　amidst　complex　weather　conditions.　This　technology　has　the　potential　to　be　widely　used　in　road　target　detection.　However,　polarization　detection　is　significantly　affected　by　illumination　and　detection　angles,　as　well　as　the　considerable　variation　in　the　scale　of　road　targets.　The　optimal　polarization　parameters　should　be　adaptively　adjusted　to　weather　conditions,　angles　and　target　features,　whereas　most　existing　research　employs　handcrafted　polarization　parameters　without　considering　actual　complex　detection　requirements,　which　are　unable　to　adaptively　adjust　the　polarization　feature　enhancement　methods.　In　this　paper,　we　propose　a　road　target　detection　algorithm　based　on　an　end-to-end　adaptive　polarization　coding　method,　named　YOLO-Polarization　of　Road　Target　Detection　(YOLO-PRTD).　To　enhance　the　polarized　features　of　targets　under　complex　weather　conditions,　an　Adaptive　Polarization　Coding　Module　(APCM)　is　designed.　This　module　integrates　channel-wise　global　self-attention　and　small　kernel　convolution　to　adaptively　adjust　the　polarization　enhancement　method　using　dynamically　extracted　global　and　local　polarization　feature　information.　A　multi-scale　detection　network　is　also　designed　to　fully　extract　and　fuse　multi-scale　feature　information　from　receptive　fields,　channels,　and　spaces　in　different　dimensions.　Additionally,　a　dataset　of　Polarized　Images　of　Road　Targets　in　Complex　Weather　conditions　(PIRT-CW)　is　proposed　for　training　and　evaluation.　Experimental　results　on　the　PIRT-CW　show　that　the　YOLO-PRTD　algorithm　achieves　a　mAP0.5　of　89.83%,　reducing　the　error　rate　by　15.54%　compared　to　the　baseline　network　YOLOX.