基于ENet的轻量级语义分割算法研究

语义分割算法能够对图像进行像素级的分类,广泛应用于无人驾驶、医学图像处理和工业自动化等领域,具有重要研究价值.对语义分割算法的研究集中在提升分割精度、降低参数量和增加推理速度3个方面.经典的轻量语义分割算法ENet使用多层卷积的编解码器和大量的空洞卷积来避免过多的下采样和利用空间信息,虽能保证一定的空间信息完整性与较大...

A lightweight semantic segmentation algorithm based on ENet

Semantic segmentation algorithms can classify images at the pixel level, and are widely used in fields such as unmanned driving, medical image processing, and industrial automation, and have important research value. The research of semantic segmentation algorithms focuses on three aspects: improving the accuracy of segmentation, reducing the amount of parameters and increasing the speed of inference. The lightweight semantic segmentation algorithm ENet uses a multi-layer convolutional codec and a large number of dilated convolutions to avoid excessive downsampling and use of spatial information. Although it retains some spatial information integrity and large receptive field, the codec is bloated, the transmission of spatial information is poor, and the sensory field overflows and causes grid effect. Aiming at the above problems, this paper tailors the ENet algorithm structure, uses the attention mechanism and the pyramid dilated convolution to design spatial information transmission module, optimizes the algorithm structure, improves the algorithm receptive field, and completely transmits the spatial information transmission. The experimental results on public datasets Cityscapes and BDD100K show that the new module can improve the performance of the original algorithm with a smaller amount of parameters and calculations, which proves the redundancy of the original algorithm and the effectiveness of the designed module.