A lightweight efficient semantic segmentation with encoder-decoder for arc interference in robotic arc welding

Abstract

Recognizing laser stripes under conditions of arc interference is a persistent challenge faced by linear structured light sensors due to their similarity with noise in morphological characteristics. In this paper, a symmetrical encoder-decoder semantic segmentation model named LSRNet (lightweight stripe recognition network) is proposed that enables real-time detection of laser stripes in environments with strong arc interference. It analyzes the difference between arc and laser stripe from the semantic point of view. Firstly, to facilitate quick and accurate recognition of laser stripes, we designed a Depthwise-separable Factorized Module that is both efficient and scalable with a larger receptive field. Moreover, due to the difficulty in distinguishing laser stripes from arcs in a local context, a Multi-scale Depthwise-separable Factorized Module is designed to capture more context information. Secondly, the Unified Attention Fusion Module is used to enhance the feature representation of the laser stripe. Additionally, The combination of DiceLoss(dice coefficient loss) and CELoss(cross-entropy loss) is introduced to optimize the loss, which addresses the pixel imbalance challenge that the pixels of the laser stripe are much less than the background pixels. Finally, based on the images collected in the industrial field, our ArcDataset is created for model training. Experimental results show that the proposed method achieves the highest intersection over union of 88.0% in laser stripe segmentation and a recognition speed of 101.3 FPS(frames per second).