When measuring the dynamic characteristics of bubbles using image-based methods, the images including complex overlapping of bubbles with irregular shape pose a huge challenge to current bubble reconstruction algorithms. Based on the Mask R-CNN architecture, a multitask model “Bubble Boundary R-CNN” is proposed for detection, segmentation and shape reconstruction of overlapping bubbles with high deformation at high gas velocities. By leveraging shared deep features from detection and segmentation, model achieves flexible bubble reconstruction and accurate size distribution extraction from high gas velocity bubble flows. To overcome the limitations of Mask R-CNN and address the challenges posed by high-deformation and overlapping bubbles, a semantic segmentation auxiliary head and the Boundary-preserving Mask Head (BMask Head) are introduced to enhance edge information and extract bubble boundary features. Additionally, an Overlapping Boundary Prediction Network (OBPN) is proposed to reconstruct boundary features that are lost due to bubble occlusion and predict the complete bubble shapes. The optimized model shows its ability to flexibly and accurately reconstruct overlapped and deformed bubbles by varying occlusion rates and circularity. The new model has also proven its reliability when applied to bubble images from a bubble column at gas velocities of up to 0.067 m3/s. In future work, more efforts are needed to further enhance the detection capability of the model and reduce missed and multiple detections at high gas velocity.