In order to solve the problems of complex scene interference , inaccurate segmentation and slow prediction ,an improved Deeplabv 3+ model for power line segmentation is proposed . Replace the original backbone network with lightweight Mobilenetv 2 network , add low -level features , obtain five -way input features , and fully extract feature information . The number of convolution in the atrous spatial pyramid pooling ( ASPP ) is increased , and the voidness rate was adjusted to improve the feature capturing ability of the image . Furthermore ,1×1 convolution operation was added after each void convolution to speed up the calculation . A coordinate attention semantic embedding branch ( CASEB) based on coordinate attention mechanism is proposed , which integrates the second and third features to enhance the representation of target features . CBAM attention module is introduced to inhibit the transmission of useless information and improve the efficiency of model recognition . Compared with the original Deeplabv 3+ model , the mean pixel attention ( MPA ) and mean intersection over union ( mloU ) of the improved model are improved by 2.37% and 3.42% respectively . This method can provide more accurate results of power line segmentation.