Abstract: With the extensive application of deep learning in medical imaging segmentation,drug detection and other medical fields,semantic segmentation technology plays a pivotal role.Semantic segmentation combines two techniques of target detection and image recognition.It aims to segment the image into multiple groups of regions with specific semantics,which is a dense classification problem at the pixel level.However,in order to promote the effective development of mobile visual recognition technology,the traditional deep learning model cannot meet the requirements of mobile devices in terms of power consumption,memory management,and real-time performance.Edge computing is a new architecture mode that effectively extends the computing,network,storage,and bandwidth capabilities from the host to the mobile edge to implement model inference operations in a limited computing resource environment.Therefore,this paper attempts to complete the transformation,deployment and inference operation of the classic image semantic segmentation model,such as FCN,SegNet,U-Net,etc,on the development board based on the edge TPU coprocessor,and verifies the correctness and performanceof the proposed semantic segmentation model on the collec-ted real drug dataset.

Key words: Deep learning, Edge Computing, Edge TPU, Semantic Segmentation