When a convolutional neural network performs real-time image semantic segmentation, processing large blocks of pixels with small color changes leads to computational spatial redundancy. Also, the accuracy of feature extraction using lightweight networks is low. We designed a real-time semantic segmentation network using the improved MobileNet v3 and lightweight OctConv high-frequency (OTCH-L) module to mitigate both problems. First, the hard-swish activation function was used to compensate for the accuracy of the lightweight network MobileNet v3. Then, we proposed an improved MobileNet v3 feature extraction network. Furthermore, we designed the OTCH-L module based on the octave convolution to solve the problem of spatial redundancy and reduce the computational size of the model while ensuring accuracy. The models were trained and verified on the Pascal VOC2012 and VOC2007 datasets, respectively. The experimental results show that the segmentation speed of the proposed model reaches 25.94 frame/s, and the mean intersection over union (MIoU) reaches 70.34%. Compared with the mainstream semantic segmentation models, such as SegNet, PSPNet, and DeepLab v3 plus, our proposed model significantly enhances the segmentation speed while maintaining segmentation accuracy.