Due　to　the　variety　and　complexity　of　objects　in　X-ray　images,　how　to　detect　the　prohibited　items　automatically　and　accurately　is　a　challenging　problem.　In　this　paper,　an　X-ray　image　prohibited　object　detection　method　based　on　Dynamic　Deformable　Convolution　(DyDC)　and　adaptive　Intersection　over　Union　(IoU)　is　proposed　based　on　Cascade　R-CNN　framework.　The　main　contributions　are　as　follows.　First,　DyDC　is　proposed　to　cope　with　the　diversity　of　the　prohibited　objects　in　X-ray　images　and　to　improve　the　feature　representation　capability.　Then,　adaptive　IoU　mechanism　is　proposed,　which　can　dynamically　adjust　the　IoU　threshold　during　the　training　process　to　generate　high　quality　proposals.　The　proposed　method　is　extensively　evaluated　on　two　publicly　available　benchmark　datasets,　namely　SIXray　and　OPIXray,　and　the　experimental　results　show　that　it　can　achieve　the　state-of-the-art　detection　accuracy,　compared　with　other　existing　methods.　©　2022　IEEE.