Name
Title | ||
|---|---|---|
Ai Enabled Iort Framework For Rodent Activity Monitoring In A False Ceiling Environment |
Abstract | ||
|---|---|---|
Routine rodent inspection is essential to curbing rat-borne diseases and infrastructure damages within the built environment. Rodents find false ceilings to be a perfect spot to seek shelter and construct their habitats. However, a manual false ceiling inspection for rodents is laborious and risky. This work presents an AI-enabled IoRT framework for rodent activity monitoring inside a false ceiling using an in-house developed robot called "Falcon". The IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The shared images by the robots are inspected through a Faster RCNN ResNet 101 object detection algorithm, which is used to automatically detect the signs of rodent inside a false ceiling. The efficiency of the rodent activity detection algorithm was tested in a real-world false ceiling environment, and detection accuracy was evaluated with the standard performance metrics. The experimental results indicate that the algorithm detects rodent signs and 3D-printed rodents with a good confidence level. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21165326 | SENSORS |
Keywords | DocType | Volume |
rodent detection, faster RCNN, deep learning, object detection, IoRT, inspection robot | Journal | 21 |
Issue | ISSN | Citations |
16 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Balakrishnan Ramalingam | 1 | 2 | 2.81 |
| Thein Tun | 2 | 0 | 0.34 |
| Rajesh Elara Mohan | 3 | 89 | 42.67 |
| Braulio Félix Gómez | 4 | 2 | 2.81 |
| Ruoxi Cheng | 5 | 0 | 0.34 |
| Selvasundari Balakrishnan | 6 | 0 | 1.01 |
| Madan Mohan Rayaguru | 7 | 0 | 0.34 |
| Abdullah Aamir Hayat | 8 | 1 | 2.40 |