Shan Chuanhui, Geng Xinlong, Han Chao
College of Electrical Engineering, Anhui Polytechnic University, Middle Beijing Road, Wuhu, 241004, Anhui Province, China.
Heliyon. 2024 Sep 6;10(18):e37470. doi: 10.1016/j.heliyon.2024.e37470. eCollection 2024 Sep 30.
Extracting and detecting road network consistency from high-resolution remote sensing images has been a hot and difficult problem in the computer vision. Although it has made significant progress, there is still a phenomenon of high training accuracy but unsatisfactory actual extraction and detection results. The attention mechanism is one of the efficient and practical mechanisms in deep learning. It improves the performance of deep learning by selectively focusing on a portion of all information while ignoring other visible information, while effectively utilizing computing resources. Numerous experiments have also confirmed that the attention mechanism is resource-saving and effective. Its plug and play feature brings great convenience to programmers. In order to provide better road network detection results and solve the above problem, this paper combines the channel attention mechanism with ResNet and proposes SE-ResNet and ECA-ResNet for remote sensing image road network detection, making networks extract and learn road network features and ignore some non-road network features. The experimental results show that on the Massachusetts roads (MR) and CHN6-CUG roads datasets, ECA-ResNet and SE-ResNet based on channel attention mechanism perform similar to LeNet7 and ResNet in terms of accuracy, loss, accuracy convergence, and loss convergence, and even increase a certain computational burden. However, their final road network detection results (including road network detection pixel count, precision, recall, accuracy, IOU, F1 score, and actual road network detection result) of the former are significantly better than those of the latter. The channel attention mechanism makes the deep neural network pay more attention to the extraction and learning of road network features, while ignoring the extraction and learning of some non-road network features, which improves the accuracy of containing road network samples and reduces the accuracy of not containing road network samples. Therefore, the performance of ECA-ResNet and SE-ResNet is similar to that of LeNet7 and ResNet in the accuracy, loss, accuracy convergence and loss convergence, but the final road network detection results of ECA-ResNet and SE-ResNet are significantly better than those of LeNet7 and ResNet. Therefore, the proposed ECA-ResNet and SE-ResNet have broad application prospects in road network detection, especially ECA-ResNet.
从高分辨率遥感影像中提取和检测道路网络一致性一直是计算机视觉领域的热点和难点问题。尽管已取得显著进展，但仍存在训练精度高但实际提取和检测结果不尽人意的现象。注意力机制是深度学习中高效实用的机制之一。它通过有选择地聚焦于所有信息的一部分，同时忽略其他可见信息来提高深度学习性能，同时有效利用计算资源。大量实验也证实了注意力机制节省资源且有效。其即插即用特性给程序员带来极大便利。为了提供更好的道路网络检测结果并解决上述问题，本文将通道注意力机制与ResNet相结合，提出了用于遥感影像道路网络检测的SE-ResNet和ECA-ResNet，使网络能够提取和学习道路网络特征，同时忽略一些非道路网络特征。实验结果表明，在马萨诸塞道路（MR）和CHN6-CUG道路数据集上，基于通道注意力机制的ECA-ResNet和SE-ResNet在准确率、损失、准确率收敛和损失收敛方面与LeNet7和ResNet表现相近，甚至增加了一定的计算负担。然而，前者最终的道路网络检测结果（包括道路网络检测像素数、精度、召回率、准确率、交并比、F1分数和实际道路网络检测结果）明显优于后者。通道注意力机制使深度神经网络更加关注道路网络特征的提取和学习，同时忽略一些非道路网络特征的提取和学习，这提高了包含道路网络样本的准确率，降低了不包含道路网络样本的准确率。因此，ECA-ResNet和SE-ResNet在准确率、损失、准确率收敛和损失收敛方面与LeNet7和ResNet表现相似，但ECA-ResNet和SE-ResNet最终的道路网络检测结果明显优于LeNet7和ResNet。因此，所提出的ECA-ResNet和SE-ResNet在道路网络检测中具有广阔的应用前景，尤其是ECA-ResNet。
