As key-components of the urban-drainage system, storm-drains and manholes are essential to the hydrological modeling of urban basins. Accurately mapping of these objects can help to improve the storm-drain systems for the prevention and mitigation of urban floods. Novel Deep Learning (DL) methods have been proposed to aid the mapping of these urban features. The main aim of this paper is to evaluate the state-of-the-art object detection method RetinaNet to identify storm-drain and manhole in urban areas in street-level RGB images. The experimental assessment was performed using 297 mobile mapping images captured in 2019 in the streets in six regions in Campo Grande city, located in Mato Grosso do Sul state, Brazil. Two configurations of training, validation, and test images were considered. ResNet-50 and ResNet-101 were adopted in the experimental assessment as the two distinct feature extractor networks (i.e., backbones) for the RetinaNet method. The results were compared with the Faster R-CNN method. The results showed a higher detection accuracy when using RetinaNet with ResNet-50. In conclusion, the assessed DL method is adequate to detect storm-drain and manhole from mobile mapping RGB images, outperforming the Faster R-CNN method. The labeled dataset used in this study is available for future research.
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机译：作为城市排水系统的关键组成部分，风暴排水沟和沙井对城市盆地的水文建模至关重要。准确地绘制这些物体可以有助于改善风暴排水系统，以防止和减轻城市洪水。已经提出了新的深度学习（DL）方法以帮助绘制这些城市特征。本文的主要目的是评估最先进的物体检测方法Retinanet，以识别街道级RGB图像中城市地区的雨水排水和人孔。实验评估是使用2019年在六个地区捕获的297个移动式映射图像，位于巴西Mato Grosso do Sul Stume的六个地区。考虑了两种培训，验证和测试图像配置。 Reset-50和Reset-101在实验评估中采用了视网膜套装方法的两个不同特征提取器网络（即骨干网）。将结果与R-CNN法进行比较。当使用Reset-50使用RetinAnet时，结果显示出更高的检测精度。总之，评估的DL方法足以检测来自移动映射RGB图像的雨液和人孔，优于更快的R-CNN方法。本研究中使用的标记数据集可用于将来的研究。
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