Prohibited　Object　Detection　(POD)　in　X-ray　images　plays　an　important　role　in　protecting　public　safety.　Automatic　and　accurate　POD　is　required　to　relieve　the　working　pressure　of　security　inspectors.　However,　the　existing　methods　cannot　obtain　a　satisfactory　detection　accuracy,　and　especially,　the　prob-lem　of　object　occlusion　also　has　not　been　solved　well.　Therefore,　in　this　paper,　according　to　the　specific　characteristics　of　X-ray　images　as　well　as　low-level　and　high-level　features　of　Convolutional　Neural　Network　(CNN),　different　feature　enhancement　strategies　have　been　elaborately　designed　for　occluded　POD.　First,　a　learnable　Gabor　convolutional　layer　is　designed　and　embedded　into　the　low　layer　of　the　net-work　to　enhance　the　network＇s　capability　to　capture　the　edge　and　contour　information　of　object.　A　Spatial　Attention　(SA)　mechanism　is　then　designed　to　weight　the　output　features　of　the　Gabor　convolutional　layer　to　enhance　the　spatial　structure　information　of　object　and　suppress　the　background　noises　simul-taneously.　For　the　high-level　features,　Global　Context　Feature　Extraction　(GCFE)　module　is　proposed　to　extract　multi-scale　global　contextual　information　of　object.　And,　a　Dual　Scale　Feature　Aggregation　(DSFA)　module　is　proposed　to　fuse　these　global　features　with　those　of　another　layer.　To　verify　the　effec-tiveness　of　the　proposed　modules,　they　are　embedded　into　typical　one-stage　and　two-stage　object　detec-tion　frameworks,　i.e.,　Faster　R-CNN　and　YOLO　v5L,　obtaining　POD-F　and　POD-Y　methods,　respectively.　The　proposed　methods　have　been　extensively　evaluated　on　three　publicly　available　benchmark　datasets,　namely　SIXray,　OPIXray　and　WIXray.　The　experimental　results　show　that,　compared　with　existing　meth-ods,　the　proposed　POD-Y　method　can　achieve　a　state-of-the-art　detection　accuracy.　And　POD-F　can　also　achieve　a　competitive　detection　performance　among　the　two-stage　detection　methods.1　(c)　2022　Elsevier　B.V.　All　rights　reserved.