Currently,　Transformer-based　prohibited　object　detection　methods　in　X-ray　images　appear　constantly,　but　there　are　still　some　shortcomings　such　as　poor　performance　and　high　computational　complexity　for　prohibited　object　detection　with　heavily　occlusion.　Therefore,　a　coarse　to　fine　detection　method　for　prohibited　object　in　X-ray　images　based　on　progressive　Transformer　decoder　is　proposed　in　this　paper.　Firstly,　a　coarse　to　fine　framework　is　proposed,　which　includes　two　stages:　coarse　detection　and　fine　detection.　Through　adaptive　inference　in　stages,　the　computational　efficiency　of　the　model　is　effectively　improved.　Then,　a　position　and　class　object　queries　method　is　proposed,　which　improves　the　convergence　speed　and　detection　accuracy　of　the　model　by　fusing　the　position　and　class　information　of　prohibited　object　with　object　queries.　Finally,　a　progressive　Transformer　decoder　is　proposed,　which　distinguishes　high　and　low　score　queries　by　decreasing　confidence　thresholds,　so　that　high-score　queries　are　not　affected　by　low-score　queries　in　the　decoding　stage,　and　the　model　can　focus　more　on　decoding　low-score　queries,　which　usually　correspond　to　prohibited　object　with　severe　occlusion.　The　experimental　results　on　three　public　benchmark　datasets　(SIXray,　OPIXray,　HiXray)　demonstrate　that　compared　with　the　baseline　DETR,　the　proposed　method　achieves　the　state-of-the-art　detection　accuracy　with　a　21.6%　reduction　in　model　computational　complexity.　Especially　for　prohibited　objects　with　heavily　occlusion,　accurate　detection　can　be　carried　out.　©　2024　ACM.