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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Storm-Drain and Manhole Detection Using the RetinaNet Method.

Anderson Santos¹, José Marcato Junior², Jonathan de Andrade Silva¹

¹Faculty of Computer Science, Federal University of Mato Grosso do Sul, Campo Grande 79070900, MS, Brazil.

Sensors (Basel, Switzerland)

|August 14, 2020

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Deep learning accurately maps storm drains and manholes using RetinaNet with ResNet-50, outperforming Faster R-CNN. This aids urban flood prevention and improves drainage system modeling.

Area of Science:

Urban Hydrology
Computer Vision
Geographic Information Systems (GIS)

Background:

Storm drains and manholes are critical for urban hydrological modeling and flood mitigation.
Accurate mapping of these features is essential for effective urban drainage system management.
Deep learning (DL) offers promising solutions for automated feature extraction in urban environments.

Discussion:

This study evaluates the RetinaNet object detection model for identifying storm drains and manholes in street-level RGB images.
The performance of RetinaNet, utilizing ResNet-50 and ResNet-101 backbones, was assessed using mobile mapping data from Campo Grande, Brazil.
Results indicate superior detection accuracy with RetinaNet employing the ResNet-50 backbone compared to Faster R-CNN.

Key Insights:

Keywords:

convolutional neural network object detection urban floods mapping

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RetinaNet with ResNet-50 demonstrates high accuracy in detecting storm drains and manholes from mobile mapping imagery.
The DL approach effectively identifies key urban drainage components, crucial for hydrological analysis.
The study validates the suitability of DL for mapping urban infrastructure from readily available RGB images.

Outlook:

The developed methodology and labeled dataset can advance research in urban flood prediction and drainage system optimization.
Further research can explore other DL architectures and datasets for broader applicability.
This work contributes to the development of smarter urban infrastructure management systems.