S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems
Computer vision in consideration of automated and robotic systems has come up as a steady and robust platform in sewer maintenance and cleaning tasks. The AI revolution has enhanced the ability of computer vision and is being used to detect problems with underground sewer pipes, such as blockages an...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/6/2966 |
| _version_ | 1827747684670242816 |
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| author | Ravindra R. Patil Mohamad Y. Mustafa Rajnish Kaur Calay Saniya M. Ansari |
| author_facet | Ravindra R. Patil Mohamad Y. Mustafa Rajnish Kaur Calay Saniya M. Ansari |
| author_sort | Ravindra R. Patil |
| collection | DOAJ |
| description | Computer vision in consideration of automated and robotic systems has come up as a steady and robust platform in sewer maintenance and cleaning tasks. The AI revolution has enhanced the ability of computer vision and is being used to detect problems with underground sewer pipes, such as blockages and damages. A large amount of appropriate, validated, and labeled imagery data is always a key requirement for learning AI-based detection models to generate the desired outcomes. In this paper, a new imagery dataset S-BIRD (Sewer-Blockages Imagery Recognition Dataset) is presented to draw attention to the predominant sewers’ blockages issue caused by grease, plastic and tree roots. The need for the S-BIRD dataset and various parameters such as its strength, performance, consistency and feasibility have been considered and analyzed for real-time detection tasks. The YOLOX object detection model has been trained to prove the consistency and viability of the S-BIRD dataset. It also specified how the presented dataset will be used in an embedded vision-based robotic system to detect and remove sewer blockages in real-time. The outcomes of an individual survey conducted at a typical mid-size city in a developing country, Pune, India, give ground for the necessity of the presented work. |
| first_indexed | 2024-03-11T05:56:06Z |
| format | Article |
| id | doaj.art-3e5d8b8e3c8245679193a0826426c2b2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:56:06Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3e5d8b8e3c8245679193a0826426c2b22023-11-17T13:44:01ZengMDPI AGSensors1424-82202023-03-01236296610.3390/s23062966S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance SystemsRavindra R. Patil0Mohamad Y. Mustafa1Rajnish Kaur Calay2Saniya M. Ansari3Faculty of Engineering Science and Technology, UiT The Arctic University of Norway, 8514 Narvik, NorwayFaculty of Engineering Science and Technology, UiT The Arctic University of Norway, 8514 Narvik, NorwayFaculty of Engineering Science and Technology, UiT The Arctic University of Norway, 8514 Narvik, NorwayDepartment of E & TC Engineering, Ajeenkya D Y Patil School of Engineering, Pune 411047, IndiaComputer vision in consideration of automated and robotic systems has come up as a steady and robust platform in sewer maintenance and cleaning tasks. The AI revolution has enhanced the ability of computer vision and is being used to detect problems with underground sewer pipes, such as blockages and damages. A large amount of appropriate, validated, and labeled imagery data is always a key requirement for learning AI-based detection models to generate the desired outcomes. In this paper, a new imagery dataset S-BIRD (Sewer-Blockages Imagery Recognition Dataset) is presented to draw attention to the predominant sewers’ blockages issue caused by grease, plastic and tree roots. The need for the S-BIRD dataset and various parameters such as its strength, performance, consistency and feasibility have been considered and analyzed for real-time detection tasks. The YOLOX object detection model has been trained to prove the consistency and viability of the S-BIRD dataset. It also specified how the presented dataset will be used in an embedded vision-based robotic system to detect and remove sewer blockages in real-time. The outcomes of an individual survey conducted at a typical mid-size city in a developing country, Pune, India, give ground for the necessity of the presented work.https://www.mdpi.com/1424-8220/23/6/2966sewer monitoringS-BIRD datasetobject detectioncomputer visionYOLOX trainingAI techniques |
| spellingShingle | Ravindra R. Patil Mohamad Y. Mustafa Rajnish Kaur Calay Saniya M. Ansari S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems Sensors sewer monitoring S-BIRD dataset object detection computer vision YOLOX training AI techniques |
| title | S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems |
| title_full | S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems |
| title_fullStr | S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems |
| title_full_unstemmed | S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems |
| title_short | S-BIRD: A Novel Critical Multi-Class Imagery Dataset for Sewer Monitoring and Maintenance Systems |
| title_sort | s bird a novel critical multi class imagery dataset for sewer monitoring and maintenance systems |
| topic | sewer monitoring S-BIRD dataset object detection computer vision YOLOX training AI techniques |
| url | https://www.mdpi.com/1424-8220/23/6/2966 |
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