Image semantic segmentation is one of the most important research directions of computer vision. Compared with traditional algorithms, image segmentation based on deep-learning performs better, and can be applied to the scene understanding stage of traffic monitoring and automatic drive. However, the speed of complex segmentation network on embedded platform is too low to be practically applied. Therefore, in view of the application of traffic monitoring and automatic drive, the image segmentation network based on deep convolutional encoder-decoder architecture was used to complete the road scene segmentation on the embedded platform NVIDIA Jetson TX2. Meanwhile, in order to accelerate the network, the model was simplified and transformed to engine based on TensorRT2 provided by NVIDIA, which including plugin layers adding and CUDA parallel optimization. The experimental results show that the speed-up ratio can reach ten, which provides support for the application of the complex structure segmentation network on the embedded platform.