Aiming at the problems of high gray value of intervertebral disc image and uneven imaging image,it is difficult to capture spatial information and lack of semantic information.Taking the recognition of lumbar intervertebral disc in T2 sagittal position by magnetic resonance as the object,this paper proposes an intervertebral disc detection algorithm,TCA_CenterNet (top coordinate attention CenterNet),based on improved CenterNet model,firstly,the coordinate attention (CA) mechanism is added to the top of the backbone feature extraction network to strengthen the network′s attention to the intervertebral disc and enhance the sensitivity of the model to the target position;Secondly,deep and shallow feature fusion is used to enhance the ability of CenterNet to extract effective features,and the generalization performance of the model is improved through data enhancement.The experimental results show that the final mean average precision (mAP) 〖ＷＴＢＺ〗of the model is 81.15% and the average frame rate is 14.2 frame/s.Compared with other comparison algorithms,the improved algorithm has better accuracy and robustness.