Lightweight Real-time Semantic Segmentation Network with Efficient Transformer and CNN
This work addresses the need for efficient and accurate semantic segmentation in real-time applications like autonomous driving, though it is incremental as it builds on existing CNN and Transformer methods.
The paper tackles the problem of real-time semantic segmentation by proposing LETNet, a lightweight network that combines CNN and Transformer to improve global representation while maintaining efficiency, achieving 72.8% mIoU at 120 FPS on Cityscapes and 70.5% mIoU at 250 FPS on CamVid with only 0.95M parameters and 13.6G FLOPs.
In the past decade, convolutional neural networks (CNNs) have shown prominence for semantic segmentation. Although CNN models have very impressive performance, the ability to capture global representation is still insufficient, which results in suboptimal results. Recently, Transformer achieved huge success in NLP tasks, demonstrating its advantages in modeling long-range dependency. Recently, Transformer has also attracted tremendous attention from computer vision researchers who reformulate the image processing tasks as a sequence-to-sequence prediction but resulted in deteriorating local feature details. In this work, we propose a lightweight real-time semantic segmentation network called LETNet. LETNet combines a U-shaped CNN with Transformer effectively in a capsule embedding style to compensate for respective deficiencies. Meanwhile, the elaborately designed Lightweight Dilated Bottleneck (LDB) module and Feature Enhancement (FE) module cultivate a positive impact on training from scratch simultaneously. Extensive experiments performed on challenging datasets demonstrate that LETNet achieves superior performances in accuracy and efficiency balance. Specifically, It only contains 0.95M parameters and 13.6G FLOPs but yields 72.8\% mIoU at 120 FPS on the Cityscapes test set and 70.5\% mIoU at 250 FPS on the CamVid test dataset using a single RTX 3090 GPU. The source code will be available at https://github.com/IVIPLab/LETNet.