Automated　security　screening　has　a　significant　role　in　protecting　public　spaces　from　security　threats　by　employing　X-ray　images　to　detect　prohibited　items.　However,　there　are　challenges　of　noise　production　due　to　squeezing,　occlusion,　and　penetration　of　luggage　objects.　Additionally,　the　hues　of　objects　are　monotonous　and　lack　luster.　To　solve　these　problems,　we　propose　an　Autonomous　Baggage　Threat　Detection　Network　(ABTD-Net)　for　accurate　prohibited　item　detection.　To　tackle　the　difficulty　of　capturing　distinctive　visual　features,　we　constructed　a　Feature　Adjustment　Head　(FAH)　to　refine　pyramid　features.　Specifically,　we　designed　an　Attention　Module　(AM)　at　several　places　after　initially　using　a　Dense　Unidirectional　Propagation　(DUP)　to　filter　noise.　Furthermore,　we　created　a　Feature　Fusion　Head　(FFH)　that　dynamically　fuses　hierarchical　visual　information　under　object　occlusion,　including　early-fusion　and　late-fusion.　Extensive　experiments　on　security　inspection　Xray　datasets　OPIXray　and　HiXray　demonstrate　the　superiority　of　our　proposed　method.