# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 6 Conclusions and Future Work  
In this paper, we proposed, designed, implemented and deployed a novel real-time flood monitoring system driven by computer vision, edge computing and an IoT system architecture. We used RealSense D455's stereo vision capabilities to extract depth from a complex medium such as water, which provided great insights into the application of RGB-D cameras in flood monitoring. The RGB-D camera was integrated with an edge computing device (Jetson Nano), to continuously process and log all critical incoming RGB-D image sensor data. The performance of the deployed system was analysed and compared with calibrated specialised ultrasonic sensor data. The system was deployed and tested under a variety of weather and lighting conditions in an actual flood mitigation facility.  
The experimental results demonstrated the promising potential of using the proposed solution to perform water level measurement using a computer-vision-based sensor. Results obtained during the night highlighted the stereoscopic capabilities of the RealSense D455, where it performed well under no visible lighting. Furthermore, running the prototype throughout the experimental period of more than 24 weeks demonstrated the reliability and capability of the proposed system. The implementation of a real-time flood monitoring system at the edge demonstrated the potential and benefits of installing similar systems in resource-constrained environments and without external measurement guide or additional hardware.  
Future work will investigate combining and superimposing RGB images, artefacts and cues onto depth maps to improve the overall accuracy and reliability of the system. To enable the system to be more dynamic and applicable at any water site, the incorporation of the internal inertial measurement unit (IMU) readings of the camera and frame subtraction techniques will be investigated. Our work can serve as a reference and further be expanded  
Figure 14: Bandwidth consumed uploading processed edge readings to an IoT dashboard (in the order of Kbps).  
into different application domains, such as those in smart cities and smart manufacturing. For instance, the images captured by the image sensor could be used to detect illegal intrusion or vandalism attempt around the facility. Images and footage from multiple stereoscopic cameras at the same location could be utilised for sensor fusion. Additional stereoscopic depth cameras may be useful in providing information over a larger area and assist in differentiating among errors during various operational conditions, which may potentially be advantageous. Image information could also be used to detect river pollution or illegal dumping activities along the river streams which would be advantageous to the relevant authorities insofar as taking pre-emptive action for containing the effect of the pollution.  
Conceptualization, O.R.J. and H.S.J.; methodology, O.R.J. and H.S.J.; software, O.R.J.; validation, O.R.J., H.S.J., R.S.J. and J.K.; formal analysis, O.R.J.; investigation, O.R.J. and H.S.J.; resources, H.S.J.; data curation, O.R.J., H.S.J. and R.S.J.; writing--original draft preparation, O.R.J. and H.S.J.; writing--review and editing, H.S.J. and J.K.; visualization, O.R.J., H.S.J., R.S.J. and J.K.; supervision, H.S.J. and R.S.J.; project administration, H.S.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.