基于改进TransUNet的沥青路面病害检测研究

Research on Asphalt Pavement Disease Detection Based on Improved TransUNet

本文研究了基于图像分割算法的沥青路面病害检测问题,提出了一种基于TransUNet的分割网络,用于沥青路面病害的检测和识别。该网络在TransUNet的骨干网络中引入了ECA注意力机制,以增强特征的表达能力和自适应性。本文使用Dice评价系数作为评价指标,对路面裂缝和坑洞进行分割,并在公开的路面病害数据集上进行了实验。实验结果表明,所提出的TransUNet-RD模型mIoU和Dice等方面优于原始的TransUNet模型和UNet模型,证明了其在沥青路面病害检测方面的有效性和优越性。

This paper studies the problem of asphalt pavement disease detection based on image segmentation algorithms, and proposes a segmentation network based on TransUNet for asphalt pavement disease detection and recognition. The network introduces the ECA attention mechanism in the back-bone network of TransUNet, to enhance the feature expression ability and adaptability. This paper uses the Dice coefficient as the evaluation index, segments the pavement cracks and potholes, and conducts experiments on the public pavement disease dataset. The experimental results show that the proposed TransUNet-RD model is superior to the original TransUNet model and UNet model in terms of mIoU and Dice, proving its effectiveness and superiority in asphalt pavement distress detection.