基于卷积神经网络的低参数量实时图像分割算法

Low-Parameter Real-Time Image Segmentation Algorithm Based on Convolutional Neural Network

提出了一种低参数量实时图像语义分割网络模型Atrous-squeezeseg。模型在最低参数量为2.1×107时的运算帧率为45.3frame/s,像素点准确度与均交并比分别可达到59.5%与62.9%。同时,嵌入式设备NVIDIA TX2的运算帧率可达8.3frame/s。实验结果表明,相比于其他分割算法,所提模型的速度和参数量均得到了提升。

We propose a real-time image semantic segmentation network model, which is named as Atrous-squeezeseg. Under the condition that the minimum parameter of the model is 2.1×107, the operation frame rate is 45.3 frame/s, and the pixel point accuracy and mean intersection over union can reach 59.5% and 62.9%, respectively. At the same time, in the embedded device NVIDIA TX2, the operate frame rate is up to 8.3 frame/s. The experimental results show that, compared with other segmentation algorithms, the speed and parameter quantity of the proposed model are increased.