DCN-Deeplabv3+: A Novel Road Segmentation Algorithm Based on Improved Deeplabv3+

Abstract

Road segmentation is an important task in the field of semantic segmentation, and the Deeplabv3+ algorithm, which is commonly used for road segmentation, has shortcomings, such as numerous parameters and a tendency to lose detailed information. Therefore, this paper proposes DCN-Deeplabv3+, an improved road segmentation algorithm with dual attention modules based on the Deeplabv3+ network, aiming to reduce the model parameters and computation while improving the segmentation accuracy. (1) MobileNetV2 is used as the backbone network to reduce model parameters and memory consumption. (2) DenseASPP+SP is used for multi-scale information fusion to obtain a larger sensory field for improved model performance. (3) The deep learning model’s understanding of the spatial structure of the input data is enhanced by using CA (coordinate attention) to improve the model’s performance in dealing with spatial structure-related tasks. (4) The neural attention mechanism (NAM) is applied to better focus on key regions in the image, thereby improving the accuracy of target detection. The experimental results show that mIoU and mPA are improved by 1.20% and 2.30% on the PASCAL VOC 2012 dataset, mIoU and mPA are improved by 3.15% and 3.90% on the Cityscapes dataset, respectively. It can be concluded that the method proposed in this paper outperforms the baseline method and has excellent segmentation accuracy on roads.

Keywords

• Road segmentation
• Deeplabv3+
• multi-scale information fusion
• attention mechanism