用专业英语翻译下面这段话：本研究在训练结束后在实际环境中进行测试与使用发现一个重要的问题由于本研究将整个路面情况进行分类但是每个路面的情况都是千变万化的并且根据绿化等多方面因素在路面每个边缘部分都有不同的绿化情况并带有土壤等元素。从而本研究可能将其分类结果造成误判的情况。所以本研究在此问题上提供了一个解决方案。解决方案为在进行使用分类功能之前将每次需要分类的图片进行预处理将每个图片进行中心裁剪将每

This study conducted testing and usage in actual environments after training, and discovered a significant problem. As this study classified the entire road surface situation, the condition of each road surface is highly variable and there are different greening situations and soil elements at each edge due to various factors such as greening. Therefore, this study may cause misjudgment of classification results. To address this issue, a solution is proposed, which involves pre-processing each image before classification by center-cropping and removing the edge parts with different greening situations, and focusing on detecting the central part. This method significantly improves the accuracy in actual environments and has great significance for the study's usage and further development, as shown in Figure 8.

The ultimate goal of this study is to achieve real-time monitoring of whether construction waste falls on the road. To achieve this, the study uses flask+nginx+uwsgi to become a network interface deployed on a cloud server, providing a channel for users to use. In actual applications, cameras and recorders provided by the waste truck operators and the government are installed at the front and rear of the vehicle to monitor the situation. By processing the recorded video into ten images per minute for monitoring and comparing the results from the front and rear cameras, it is possible to determine whether the spillage of construction waste is caused by the vehicle and take appropriate measures