Based on the improved installation gap identification algorithm of the DeepLabV3+ spacer rod replacement robot

Abstract

This paper proposes an improved DeepLabV3+ lightweight algorithm for the identification of installation gaps in spacer replacement robots. By using lightweight MobileNetV3 to extract semantic features of spacer installation gaps, parameters and computational complexity are reduced; Perform dimensionality reduction and dimensionality increase operations on the ASPP module to reduce the number of model parameters; Introduce ECA module to restore the clarity of target boundaries; Use a loss function combining Focal Loss and Dice Loss to enhance segmentation performance. The experimental results show that the improved DeepLabV3+ algorithm improves MIoU, MPA, and prediction speed, while balancing segmentation accuracy and speed, and can effectively segment the installation gap of the spacer.

Keywords

• spacer rod replacement robot
• installation clearance
• improved deeplabv3+
• lightweight
• attention mechanism