Manhole cover detection using a geometrical filter on very high resolution aerial and satellite images

Bartoli, Olivier; Chahinian, Nanée; Allard, Aude; Bailly, Jean-Stéphane; Chancibault, Katia; Rodríguez, Fabrice; Salles, Christian; Tournoud, Marie-George; Delenne, Carole

doi:10.1109/jurse.2015.7120521

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…To correct this problem, we are considering combining the CNN to a circular filter presented in (Bartoli et al, 2015). Instead of training the network with only the three RGB channels, it will be trained on four entries with the fourth being the result obtained with the circular filter.…”

Section: Resultsmentioning

confidence: 99%

“…Recently a circular geometrical filter based method was proposed in (Bartoli et al, 2015) to detect round-shaped manhole covers in very high resolution aerial images. After the segmentation of the image to retain only the road network, colorimetric indices are used to eliminate the vegetation and shadows.…”

Section: Related Workmentioning

confidence: 99%

“…This has the advantage of providing a feedback about the current road conditions and thus avoiding accidents whether it is for intelligent transportation systems, moving vehicles or pedestrians (Yu et al, 2015). * Corresponding author carole.delenne@umontpellier.fr But these approaches are extremely expensive, need considerable post-processing and are restricted to objects located on roads.In previous works, we have attempted to detect manhole covers using aerial images and have thus demonstrated the potential of these low cost and efficient methods (Pasquet et al, 2016, Bartoli et al, 2015.…”

Section: Introductionmentioning

confidence: 99%

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Manhole Cover Localization in Aerial Images With a Deep Learning Approach

Commandré

En-Nejjary

Pibre

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Urban growth is an ongoing trend and one of its direct consequences is the development of buried utility networks. Locating these networks is becoming a challenging task. While the labeling of large objects in aerial images is extensively studied in Geosciences, the localization of small objects (smaller than a building) is in counter part less studied and very challenging due to the variance of object colors, cluttered neighborhood, non-uniform background, shadows and aspect ratios. In this paper, we put forward a method for the automatic detection and localization of manhole covers in Very High Resolution (VHR) aerial and remotely sensed images using a Convolutional Neural Network (CNN). Compared to other detection/localization methods for small objects, the proposed approach is more comprehensive as the entire image is processed without prior segmentation. The first experiments using the Prades-Le-Lez and Gigean datasets show that our method is indeed effective as more than 49% of the ground truth database is detected with a precision of 75%. New improvement possibilities are being explored such as using information on the shape of the detected objects and increasing the types of objects to be detected, thus enabling the extraction of more object specific features.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Manhole Cover Localization in Aerial Images With a Deep Learning Approach

Commandré

En-Nejjary

Pibre

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Bartoli et al [20] reconstructed urban drainage networks by detecting manhole covers using a circular detection filter on orthophotos and satellite images. Pasquet et al [21] identified the presence of underground utilities by locating manhole covers and stormwater drains from high-resolution aerial images using a linear support vector machine (SVM) classifier and a histogram of oriented gradients (HOG) that performed similarly to the circular detection filter used in Bartoli et al [20].…”

Section: Aerial Mappingmentioning

confidence: 99%

Review of Artificial Intelligence Applications for Virtual Sensing of Underground Utilities

Oguntoye

Laflamme

Sturgill

et al. 2023

Sensors

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Accurately identifying the location and depth of buried utility assets became a considerable challenge in the construction industry, for which accidental strikes can cause important economic losses and safety concerns. While the collection of as-built utility locations is becoming more accurate, there still exists an important need to be capable of accurately detecting buried utilities in order to eliminate risks associated with digging. Current practices typically involve the use of trained agents to survey and detect underground utilities at locations of interest, which is a costly and time-consuming process. With advances in artificial intelligence (AI), an opportunity arose in conducting virtual sensing of buried utilities by combining robotics (e.g., drones), knowledge, and logic. This paper reviewed methods that are based on AI in mapping underground infrastructure. In particular, the use of AI in aerial and terrestrial mapping of utility assets was reviewed, followed by a summary of AI techniques used in fusing multi-source data in creating underground infrastructure maps. Key observations from the consolidated literature were that (1) when leveraging computer vision methods, automatic mapping techniques vastly focus on manholes localized from aerial imagery; (2) when applied to non-intrusive sensing, AI methods vastly focus on empowering ground-penetrating radar (GPR)-produced data; and (3) data fusion techniques to produce utility maps should be extended to any utility assets/types. Based on these observations, a universal utility mapping model was proposed, one that could enable mapping of underground utilities using limited information available in the form of different sources of data and knowledge.

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“…However in, the above references show that how to detect the manholes previously. The authors in [3] only able to detect the cover of manholes using a geometrical filter on very high resolution aerial and satellite images by using gray color object detection and the success rate is very low about 42%. In [4] two techniques is applied to detect the manholes first one searches the circular pattern using the Bhattacharyya coefficient while another one uses for the machine learning to construct a model of manhole cover the accuracy rate of this algorithm is also not good only 40% covers were detected.…”

Section: Introductionmentioning

confidence: 99%

SURF and LA with RGB Vector Space Based Detection and Monitoring of Manholes with an Application to Tri-Rotor UAS Images

Ali¹,

Nanjing²,

Wang³

et al. 2017

IJET

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The detection of manhole's in a road by using Unmanned Aerial System or Vehicle (UAS) offers significant precision, however detection of manholes in above ground level images is a difficult task, with the help of Digital Single Lens Reflex (DSLR) camera and by using an image processing technique it is quite easy to detect small objects from the aerial view. Previously many methods is used to detect the manhole's covers, and many of them is quite accurate about 90% accuracy rate. But all the method applied for the detection of a covered manhole, but no one detects if there is no manhole cover lie in a manhole frame. This manuscript we present Speeded-Up Robust Feature's (SURF) algorithm for the key point detection of the manhole cover is present or not, and by marking many key points detects the manhole. After that cross checking our algorithm accuracy by using another method Learning Automata (L.A) with color gradient vector Red, Green, Blue (RGB) for the detection of a circular shape of manhole cover and color recognition for the cover is present or not.Index Terms-Manhole detection, speeded-up robust feature, Learning automata, RGB vector space.

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Manhole cover detection using a geometrical filter on very high resolution aerial and satellite images

Cited by 6 publications

References 16 publications

Manhole Cover Localization in Aerial Images With a Deep Learning Approach

Manhole Cover Localization in Aerial Images With a Deep Learning Approach

Review of Artificial Intelligence Applications for Virtual Sensing of Underground Utilities

SURF and LA with RGB Vector Space Based Detection and Monitoring of Manholes with an Application to Tri-Rotor UAS Images

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