Improved Model for Table-Line Detection Based on YOLOv8n
In the task of table recognition and reconstruction,the methods of segmentation and merging rely on the detec-tion of table lines to reconstruct electronic spreadsheets.Consequently,the quality of table line detection result directly determines the efficacy of the table reconstruction.To address the issues of false and missed detections in existing methods,an enhanced YOLOv8n model for table line detection is proposed.In the backbone network,the BottleneckCSP module is refined using the Swin Transformer methodology,enabling the capture of extended-range contextual information and aug-menting the recognition capability for large-scale table lines.Given the elongated and dense nature of the table line,the C2f(CSPLayer_2 Conv)module is enhanced with the concept of snake dynamic convolution,which adaptively adjusts the shape and position of the convolution kernel according to the spatial relationships among features.This enhancement more effectively captures the correlation and local details between features,thereby improving the feature modeling capa-bility.Furthermore,the spatial pyramid pooling layer is modified using the CBAM(convolutional block attention module)attention mechanism,in order to dynamically adjust the significance of each channel and spatial position within the fea-ture map,thus enhancing the discriminative capacity of the feature map.The neck structure is optimized and reconstructed by incorporating shuffle convolution.The experimental results indicate that the improved YOLOv8n model achieves increases of 0.079、0.301、0.088 in mAP@0.5:0.95,precision and recall respectively,on the ICDAR_2013 and PubTabNet datasets,exceeding the performance of other YOLO series models and demonstrating effective application to table line detection tasks.These improvements enable the YOLOv8n model to exhibit superior performance in the task of table line detection.By integrating with the merging method,the effectiveness of table reconstruction can be further enhanced.
万方数据
维普
