Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning
Abstract This work proposes the use of an unmanned aerial vehicle (UAV) with an autopilot to identify the defects present in municipal sewerage pipes. The framework also includes an effective autopilot control mechanism that can direct the flight path of a UAV within a sewer line. Both of these brea...
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Wiley
2025-01-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.12852 |
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| author | Binay Kumar Pandey Digvijay Pandey S. K. Sahani |
| author_facet | Binay Kumar Pandey Digvijay Pandey S. K. Sahani |
| author_sort | Binay Kumar Pandey |
| collection | DOAJ |
| description | Abstract This work proposes the use of an unmanned aerial vehicle (UAV) with an autopilot to identify the defects present in municipal sewerage pipes. The framework also includes an effective autopilot control mechanism that can direct the flight path of a UAV within a sewer line. Both of these breakthroughs have been addressed throughout this work. The UAV's camera proved useful throughout a sewage inspection, providing important contextual data that helped analyze the sewerage line's internal condition. A plethora of information useful for understanding the sewerage line's inner functioning and extracting interior visual details can be obtained from camera‐recorded sewerage imagery if a defect is present. In the case of sewerage inspections, nevertheless, the impact of a false negative is significantly higher than that of a false positive. One of the trickiest parts of the procedure is identifying defective sewerage pipelines and false negatives. In order to get rid of the false negative outcome or false positive outcome, a guided image filter (GIF) is implemented in this proposed method during the pre‐processing stage. Afterwards, the algorithms Gabor transform (GT) and stroke width transform (SWT) were used to obtain the features of the UAV‐captured surveillance image. The UAV camera's sewerage image is then classified as “defective” or “not defective” using the obtained features by a Weighted Naive Bayes Classifier (WNBC). Next, images of the sewerage lines captured by the UAV are analyzed using speed‐up robust features (SURF) and deep learning to identify different types of defects. As a result, the proposed methodology achieved more favorable outcomes than prior existing approaches in terms of the following metrics: mean PSNR (71.854), mean MSE (0.0618), mean RMSE (0.2485), mean SSIM (98.71%), mean accuracy (98.372), mean specificity (97.837%), mean precision (93.296%), mean recall (94.255%), mean F1‐score (93.773%), and mean processing time (35.43 min). |
| format | Article |
| id | doaj-art-fdea091b04124bc4835907dec47bd8ff |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-fdea091b04124bc4835907dec47bd8ff2025-01-31T00:22:48ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.12852Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learningBinay Kumar Pandey0Digvijay Pandey1S. K. Sahani2Department of Information Technology College of Technology, Govind Ballabh Pant University of Agriculture and Technology Panatnagar IndiaDepartment of Technical Education Kanpur IndiaDepartment of Mathematics MIT Campus, (T.U.) Janakpurdham NepalAbstract This work proposes the use of an unmanned aerial vehicle (UAV) with an autopilot to identify the defects present in municipal sewerage pipes. The framework also includes an effective autopilot control mechanism that can direct the flight path of a UAV within a sewer line. Both of these breakthroughs have been addressed throughout this work. The UAV's camera proved useful throughout a sewage inspection, providing important contextual data that helped analyze the sewerage line's internal condition. A plethora of information useful for understanding the sewerage line's inner functioning and extracting interior visual details can be obtained from camera‐recorded sewerage imagery if a defect is present. In the case of sewerage inspections, nevertheless, the impact of a false negative is significantly higher than that of a false positive. One of the trickiest parts of the procedure is identifying defective sewerage pipelines and false negatives. In order to get rid of the false negative outcome or false positive outcome, a guided image filter (GIF) is implemented in this proposed method during the pre‐processing stage. Afterwards, the algorithms Gabor transform (GT) and stroke width transform (SWT) were used to obtain the features of the UAV‐captured surveillance image. The UAV camera's sewerage image is then classified as “defective” or “not defective” using the obtained features by a Weighted Naive Bayes Classifier (WNBC). Next, images of the sewerage lines captured by the UAV are analyzed using speed‐up robust features (SURF) and deep learning to identify different types of defects. As a result, the proposed methodology achieved more favorable outcomes than prior existing approaches in terms of the following metrics: mean PSNR (71.854), mean MSE (0.0618), mean RMSE (0.2485), mean SSIM (98.71%), mean accuracy (98.372), mean specificity (97.837%), mean precision (93.296%), mean recall (94.255%), mean F1‐score (93.773%), and mean processing time (35.43 min).https://doi.org/10.1002/eng2.12852Gabor transformguided image filtersewagestroke width transformSURFSWT |
| spellingShingle | Binay Kumar Pandey Digvijay Pandey S. K. Sahani Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning Engineering Reports Gabor transform guided image filter sewage stroke width transform SURF SWT |
| title | Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning |
| title_full | Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning |
| title_fullStr | Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning |
| title_full_unstemmed | Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning |
| title_short | Autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning |
| title_sort | autopilot control unmanned aerial vehicle system for sewage defect detection using deep learning |
| topic | Gabor transform guided image filter sewage stroke width transform SURF SWT |
| url | https://doi.org/10.1002/eng2.12852 |
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