In order to improve the pixel segmentation accuracy of the current semantic segmentation model the algorithm complexity continues to increase resulting in a large number of parameters time-consuming and difficulty in deploying to industrial sites. A bar segmentation algorithm based on the lightweight MobileNetV2-DeeplabV3+ model was proposed. The algorithm made a series of improvements based on the original network in order to balance the pixel segmentation accuracy the number of model parameters and the detection speed of the algorithm. The original Xception backbone network was replaced with a lightweight MobileNetV2 network to reduce the number of model parameters and computational complexity. On the basis of the Atrous Spatial Pyramid Pooling ASPP module the atrous convolutions were densely connected to obtain a larger receptive field and denser pixel sampling and to enlarge the semantic information covered by the output features. The computational complexity of the model was further reduced by using deep separable convolution DSConv instead of the standard convolution in the ASPP module. In addition an effective channel attention ECA module was introduced to focus on the target edge features and enhance the effect of channel information extraction in the feature maps. The experiment showed that the improved model achieved a mean intersection over Union MIOU of 89. 37% a mean pixel accuracy MPA of 94. 57% a frame rate of 33. 09 frames per second FPS and a model parameter size of 33. 6 M on the bar dataset. Compared with the models of U-net MPSPNet and MDeeplabV3+ the MIOU and MPA values of the improved algorithm were slightly lower than the best values but still at a high level with a small number of model parameters and a significant increase in FPS value. The example shows that the improved algorithm can better balance the segmentation accuracy and the real-time performance of the algorithm and can meet the needs of deployment to industrial sites.