Due　to　its　insensitivity　to　light　intensity　and　the　capability　to　capture　multidimensional　information,　polarimetric　imaging　technology　has　been　proven　to　have　advantages　over　traditional　intensity-based　imaging　techniques　for　object　detection　tasks　in　adverse　environmental　conditions,　particularly　in　road　traffic　scenarios.　Recently,　with　the　rapid　development　of　artificial　intelligence　technology,　deep　learning　(DL)-powered　object　detection　techniques　can　further　enhance　recognition　accuracy　and　algorithm　robustness.　This　improvement　is　made　possible　by　the　ability　of　DL　technology　to　leverage　large　datasets　and　extract　deeper　levels　of　target-specific　features.　However,　constructing　large-scale　polarimetric　datasets　poses　challenges　as　obtaining　polarimetric　information　requires　multiple　captures　of　intensity　images.　In　other　words,　the　workload　is　several　times　higher　compared　to　traditional　imaging　techniques.　To　address　the　current　scarcity　of　polarimetric　datasets　and　evaluate　the　practical　performance　of　various　algorithms　on　polarimetric　datasets,　this　paper　proposes　a　Polarimetric　Object　Detection　Benchmark　(PODB)　dataset.　The　PODB　provides　an　integrated　quality　evaluation　framework　for　DL-based　object　detection　algorithms　in　complex　road　scenes　by　incorporating　polarimetric　imaging.　Besides,　we　conducted　extensive　object　detection　experiments　using　the　PODB,　which　allowed　for　a　comprehensive　validation　and　performance　evaluation　of　effective　benchmark　algorithms.　Furthermore,　a　physics-based　multi-scale　image　fusion　cascaded　object　detection　neural　network　model　is　proposed.　By　combining　the　multidimensional　information　provided　by　polarized　images　with　an　adaptive　learning　multi-decision　object　detection　neural　network　model,　the　recognition　accuracy　of　complex　road　scenes　in　adverse　weather　conditions　has　been　improved　by　approximately　10%.　Additionally,　we　anticipate　that　PODB　will　serve　as　an　effective　platform　for　evaluating　and　comparing　the　performance　of　object　detection　algorithms,　as　well　as　providing　researchers　with　a　baseline　for　future　studies　in　developing　new　DL-based　methods.　©　2024　Elsevier　B.V.