[PDF] Sewer Inlet Localization in UAV Image Clouds: Improving Performance with Multiview Detection | Semantic Scholar (2024)

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This paper presents a fully automatic storm drain detection method to extract and locate storm drain inlets in mobile mapping lidar data and achieves high levels of accuracy and localisation.

This work presents a fully automatic manhole cover detection method to extract and accurately determine the position of manhole covers from mobile mapping point cloud data and assesses the influence of different backbone architectures and that of the geometric information channels in the ground image when commonly only the intensity channel is used.

The assessed DL method RetinaNet is adequate to detect storm-drain and manhole from mobile mapping RGB images, outperforming the Faster R-CNN method.

A method to capture road drainage covers through images and assess if they could potentially be blocked or not and a novel framework focuses on two main tasks: a) localisation of road drainage b) assessment of drainage condition.

The proposed multi-step method based on geometric and radiometric constraints on manhole covers for their extraction and maintenance-related information measurement performed satisfactorily in the challenging cases of road manholes covers and achieves significant improvement in comparison with several state-of-the-art methods.

The probabilistic pipeline mapping model (PPMM) is proposed to map the buried pipelines from the conducted pipeline dataset, with or without statutory pipeline records.

An overview of state-of-the-art works on robot simultaneous localization and mapping (SLAM) with a focus on water and sewer pipe networks considers various aspects of the SLAM problem in pipes, from the motivation, to the water industry requirements, modern SLAM methods, map-types and sensors suited to pipes.

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This paper 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).

A novel method to update assets for telecommunication infrastructure using google street view (GSV) images using HOG descriptors with SVM, Deformable parts model (DPM), and Deep learning using faster RCNNs is presented.

The detection results obtained from the road surface point clouds acquired by a RIEGL VMX-450 system show that the manhole and sewer well covers can be detected automatically and accurately.

A methodology to detect manhole covers and grates on very high-resolution aerial and satellite images and two methods are tested: the first is based on a geometrical circular filter, whereas the second one uses machine learning to retrieve some patterns.

The results show that road surfaces are correctly segmented, and street light poles are robustly extracted with a completeness exceeding 99%, a correctness exceeding 97%, and a quality exceeding 96%, thereby demonstrating the efficiency and feasibility of the proposed algorithm to segment road surfaces and extract street light pole from huge volumes of mobile LiDAR point-clouds.

This work introduces a solution that adapts state-of-the-art CNN-based object detectors and classifiers, and shows that combining multiple views significantly improves both tree detection and tree species classification, rivaling human performance.

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