Fast　and　accurate　prohibited　object　detection　in　X-ray　images　is　great　challenging.　Based　on　YOLOv6　object　detection　framework,　in　this　paper,　Channel-Target　Attention　Feature　Pyramid　Network　(CTA-FPN)　is　proposed　for　prohibited　object　detection　in　X-ray　images.　It　includes　two　key　components:　TAAM　(Target　Aware　Attention　Module)　and　CAM　(Channel　Attention　Module).　TAAM　is　to　generate　the　target　attention　map　to　enhance　the　features　of　prohibited　object　regions　and　suppress　those　of　the　background　regions,　so　as　to　solve　the　problems　of　object　occlusion　and　cluttered　background　in　X-ray　images.　CAM　is　to　highlight　the　feature　channels　important　to　the　detection　tasks,　and　suppress　the　irrelevant　ones.　The　target-wise　and　channel-wise　feature　enhancement　can　effectively　strengthen　the　feature　representation　capability　of　the　network.　The　proposed　CTA-FPN　is　incorporated　into　S,　M　and　L　models　of　YOLOv6　respectively,　obtaining　three　X-ray　prohibited　object　detection　models.　The　experimental　results　on　two　publicly　available　benchmark　datasets　of　SIXray　and　CLCXray　show　that,　CTA-FPN　can　effectively　improve　the　detection　performance　of　YOLOv6.　Especially,　YOLOv6-CTA-FPN-L　can　achieve　the　state-of-the-arts　detection　accuracy.　©　2023,　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.