There is limited research on semantic segmentation of pulse diagrams using deep learning models.Therefore,an improved DeepLabv3+algorithm specifically for semantic segmentation of pulse diagrams is proposed.DeepLabv3+is served as the basic framework model,with its backbone network replaced by MobileNetV2 in the coding structure.This modification effectively reduces model size and parameter redundancy.Moreover,an improved bottleneck series fusion multi-scale pyramid pooling module is constructed to effectively expand the receptive field,improve segmentation accuracy,and further reduce parameter redundancy.An improved channel-spatial serial attention mechanism is integrated into the decoding structure for improving model performance.Concurrently,the sampling multiple is reduced for mitigating feature information loss.The improved DeepLabv3+model for semantic segmentation of pulse diagrams demonstrates exceptional capability in simultaneously and quickly detecting and identifying pulse curves and multiple elements in pulse diagrams.With a segmentation speed of 98.7 frames/s,the model achieves excellent performance metrics including 95.35%MPA and 88.66%MIoU,while maintaining a compact size of only 16.8 MB.Notably,the visualized segmentation results confirmes the superior performance of the proposed model over other models.These results suggest that the proposed model is particularly well-suited for efficient and fast semantic segmentation of pulse diagrams.

There is limited research on semantic segmentation of pulse diagrams using deep learning models.Therefore,an improved DeepLabv3+algorithm specifically for semantic segmentation of pulse diagrams is proposed.DeepLabv3+is served as the basic framework model,with its backbone network replaced by MobileNetV2 in the coding structure.This modification effectively reduces model size and parameter redundancy.Moreover,an improved bottleneck series fusion multi-scale pyramid pooling module is constructed to effectively expand the receptive field,improve segmentation accuracy,and further reduce parameter redundancy.An improved channel-spatial serial attention mechanism is integrated into the decoding structure for improving model performance.Concurrently,the sampling multiple is reduced for mitigating feature information loss.The improved DeepLabv3+model for semantic segmentation of pulse diagrams demonstrates exceptional capability in simultaneously and quickly detecting and identifying pulse curves and multiple elements in pulse diagrams.With a segmentation speed of 98.7 frames/s,the model achieves excellent performance metrics including 95.35%MPA and 88.66%MIoU,while maintaining a compact size of only 16.8 MB.Notably,the visualized segmentation results confirmes the superior performance of the proposed model over other models.These results suggest that the proposed model is particularly well-suited for efficient and fast semantic segmentation of pulse diagrams.