Contextual Guidance Network for Real-Time Semantic Segmentation of Autonomous Driving

With the rise of mobile computing and the increasing demand for real-time applications, the need for efficient and accurate semantic segmentation models has become paramount. However, existing state-of-the-art models are often hindered by heavy computational requirements, rendering them impractical for real-time applications. To tackle this challenge, we introduce the Contextual Guidance Network (CGNet), an efficient and lightweight network designed specifically for real-time semantic segmentation in autonomous driving. CGNet primarily consists of two key components: the Contextual Guidance Module (CGM) and the Triple-Branch Residual Fusion Module (TRFM). The CGM is comprised of the Downsampling Refine Unit (DRU) and the Contextual Guidance Bottleneck (CGB), which are utilized to gather dense contextual information. The DRU functions as a downsampling tool to generate low-resolution images, while the CGB extracts rich contextual information from both spatial and channel dimensions. Additionally, the TRFM utilizes the Residual Fusion Module (RFM) to achieve feature fusion and enhance pixel prediction accuracy. Without bells and whistles, CGNet achieves impressive mean intersection over union (mIoU) scores of 77.11% with 1.00 million parameters at 86.71 frames per second (fps) on the Cityscapes dataset, 72.26% mIoU at 88.62 fps on the CamVid dataset, and 63.32% mIoU on the BDD100K dataset. Extensive experiments demonstrate that CGNet achieves a favorable tradeoff between segmentation accuracy, inference speed and computational cost, making it suitable for autonomous driving systems with limited hardware resources. The source code will be available on GitHub: https://github.com/lv881314/CGNet