Current digital office documents involve huge amount of table data. Accordingly, increasing demand on intelligent table recognition emerges. Nonetheless, the table structure is complex and closely linked, leading to the ultrahigh difficulty in table structure detection. To address this problem, a new table row-column cell structure detection method was proposed based on YOLOv8, in which the ICDAR19-cTDaR table cell structure and the TabStructDB table row-column structure were taken as object. Firstly, in order to enhance the extraction of table cells and row and column features, this paper introduced deformable convolution network (DCN). Secondly, the introduction of spatial and channel reconstruction convolution (SCConv) not only had a strong feature extraction capability, but also reduced redundant features to reduce the complexity and computational cost. Based on the above introduced convolution, a new module DSC module was designed to replace the Bottlenck module in C2f and named as C2fDSC module. Additionally, in order to further enhance the corner local feature extraction of the table structure, a explicit visual center feature adjustment (EVC) module was added to the backbone network of YOLOv8. Finally, the loss function of the original model was replaced by MPDIoU. When the problem of dense objective regression accuracy is being solved, the MPDIoU loss function bounding box regression is more accurate and efficient compared to the original model loss function. Experimental results show that the table structure detection algorithm in the dataset ICDAR19-cTDaR achieves the best detection results so far. The cell checking rate, checking rate and F1 value are 91.7%, 82.3% and 86.7%, respectively. Moreover, the proposed algorithm also performed well in the data-set TabStructDB table row and column detection. [ABSTRACT FROM AUTHOR]