用专业英语翻译下面这段话：针对路面监测任务本文提出了一种基于ResNet-50的改进模型。该模型在特征筛选、下采样和特征融合等方面进行了改进。通过标准的训练数据集选择了几种目前流行的分类模型并在准确性和时间复杂度之间进行权衡选择了ResNet-50作为基础网络模型。然后通过增加注意机制、修改下采样方法、添加迁移学习等方法对该算法进行改进。通过对自建数据集的实验对原始ResNet-50模型进行了准确

This paper proposes an improved model based on ResNet-50 for road surface monitoring tasks. The model has been improved in feature selection, downsampling, and feature fusion. Several popular classification models were selected based on a standard training dataset, and ResNet-50 was chosen as the foundation network model, taking into account the balance between accuracy and time complexity. The algorithm was then improved by adding attention mechanism, modifying downsampling methods, and adding transfer learning. Through experiments on a self-built dataset, the original ResNet-50 model was tested with an accuracy of 84.46%. After adding attention modules and improved downsampling methods in ResNet-50A model, the accuracy of the model increased to 88.4% and the loss decreased by nearly half. Finally, after adding transfer learning to the ResNet-50-A model, the accuracy of the ResNet-50-B model increased to 92.08% and the loss further decreased. However, the ultimate research result of this paper not only can improve accuracy but also can further expand the monitoring method, such as improving image classification to object detection, further improving the accuracy of practical applications. It can also be extended to practical applications beyond construction waste, without increasing the computational complexity, the focus of future research will be on improving practical applications