Anomaly　object　detection　is　the　core　technology　in　the　application　for　X-ray　images.　However,　the　accuracy　of　current　X-ray　anomaly　object　detection　method　still　needs　to　be　improved.　In　this　paper,　an　effective　anomaly　object　detection　network　is　proposed　to　improve　the　detection　accuracy　of　anomaly　object　for　X-ray　images.　Firstly,　learnable　Gabor　convolution　layer,　deformable　convolution,　and　spatial　attention　mechanism　are　introduced　to　enhance　the　representative　ability　of　features　in　ResNeXt.　Then,　dense　local　regression　is　applied　to　predict　the　offset　of　multiple　dense　boxes　in　region　proposal　to　locate　the　object　accurately.　At　last,　bigger　discriminative　RoI　pooling　is　proposed　to　classify　the　candidate　boxes　to　improve　the　accuracy　of　object　classification.　Experimental　results　on　the　SIXray　and　OPIXray　datasets　show　that　compared　with　the　state-of-the-art　methods,　the　proposed　EAOD-Net　can　achieve　the　competitive　detection　performance.