ASFNet: Adaptive multiscale segmentation fusion network for real‐time semantic segmentation

Abstract Recently, the development of deep learning has facilitated continuous progress in the field of computer vision. Pixel‐level semantic segmentation serves as a fundamental task in computer vision. It achieves significant results by connecting wider and deeper backbone networks and building fine‐grained segmentation heads. However, applications such as self‐driving cars are more critical to the computational speed of the algorithms. The trade‐off between accuracy and real‐time performance of existing algorithms is still a challenging task. To address this challenge, this article proposes an adaptive multiscale segmentation fusion network to fuse multiscale contextual, which designs an adaptive multiscale segmentation fusion module based on an attention mechanism. Using segmentation fusion instead of feature fusion, the multiscale segmentation results are aggregated to obtain more precise segmentation results. The final results achieved 70.9% mIoU of accuracy in the Cityspace test set, processing images at 61 FPS when the input is 1024 × 2048. In addition, when adjusting the input size to 512 × 1024, the images are processed at 185 FPS.