Efficient fully convolutional network for real-time semantic segmentation

Considering the increased interest for deploying semantic segmentation models in real-world applications, including autonomous driving, robotics, video surveillance, researchers are developing ultra-lightweight segmentation frameworks, comprising approximately 1 million trainable parameters. Therefore, in this study, we propose an efficient convolutional segmentation network, termed as ECSNet, which achieves a good trade-off between segmentation accuracy, inference speed, computational cost, and parameter count. Particularly, we design the dilated convolutional feature module (DCFM), that is…

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original... [625 chars]