Adaptive Attention Mechanism Fusion for Real-Time Semantic Segmentation in Complex Scenes

Realizing high accuracy and low computational burden is a serious challenge faced by Convolutional Neural Network (CNN) for real-time semantic segmentation. In this paper, an efficient real-time semantic segmentation Adaptive Attention mechanism Fusion Network(AAFNet) is designed for complex urban street scenes with numerous types of targets and large changes in lighting. Image spatial details and semantic information are respectively extracted by the network, and then, through Feature Fusion Network(FFN), accurate semantic images are obtained. Dilated Deep-Wise separable convolution (DDW) is adopted by AAFNet to increase the receptive field of semantic feature extraction, an Adaptive Attention mechanism Fusion Module (AAFM) is proposed, which combines Adaptive average pooling(Avp) and Adaptive max pooling(Amp) to refine the edge segmentation effect of the target and reduce the leakage rate of small targets. Finally, semantic segmentation experiments are performed on the Cityscapes and CamVid datasets for complex urban street scenes. The designed AAFNet achieves 73.0% and 69.8% mean Intersection over Union (mIoU) at inference speeds of 32 fps (Cityscapes) and 52 fps (CamVid). Compared with Dilated Spatial Attention Network (DSANet), Multi-Scale Context Fusion Network (MSCFNet), and Lightweight Bilateral Asymmetric Residual Network (LBARNet), AAFNet has the highest segmentation accuracy. © 2024 Science Press. All rights reserved.