Distribution Line Pole Detection and Counting Based on YOLO Using UAV Inspection Line Video

Chen, Binghuang; Miao, Xin

doi:10.1007/s42835-019-00230-w

Cited by 65 publications

(34 citation statements)

References 12 publications

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“…This approach outperforms SSD and RetinaNet in terms of speed and it is competitive in terms of accuracy in detecting large objects 2 (Liu et al, 2018) (Li et al, 2020). Moreover, recent works such as (Hossain and Lee, 2019), (Sadykova et al, 2019), (Opromolla et al, 2019) and (Chen and Miao, 2020) achieved good performance in applications involving RPAs and real-time object detection using YOLOv2.…”

Section: Proposal-free (One Shot) Methodsmentioning

confidence: 90%

3d Photogrammetric Inspection of Risers Using Rpas and Deep Learning in Oil and Gas Offshore Platforms

Salazar

Buschinelli

Marcellino

et al. 2020

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

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Abstract. The purpose of this paper is to show how deep learning techniques, based on CNNs, can contribute to photogrammetry process to perform geometric inspections of risers on offshore platforms. The photogrammetry process has a problematic related to the relative movements presented in the scene where the images are being acquired (dynamic photogrammetry). As an alternative solution, this work proposes the use of the YOLOv2 architecture, because this detector complies with some requirements of speed and good performance considering the functional requisites of the study executed. Thus, the purpose of this model is to detect risers and i-tubes on offshore platforms, then extract an inspection riser from the scene. Finally, with the images obtained, a 3D reconstruction is performed, followed by the results’ analyses.

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Section: Proposal-free (One Shot) Methodsmentioning

confidence: 90%

3d Photogrammetric Inspection of Risers Using Rpas and Deep Learning in Oil and Gas Offshore Platforms

Salazar

Buschinelli

Marcellino

et al. 2020

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

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“…3D reconstruction can be employed for the visualization of PTL, which is helpful for inspectors to analyze the excursion trend of PTL corridors, interference of surrounding environment, broken point detection, and snow loading. (b) Fine inspection of pylons [4] and components [5,6]. Object detection is exploited to recognize and locate them and their defects, such as cracked nut, bolt looseness, and fitting corro-sion, which are difficult to be found by manual inspection.…”

Section: Introductionmentioning

confidence: 99%

Environment Perception Technologies for Power Transmission Line Inspection Robots

et al. 2021

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With the fast development of the power system, traditional manual inspection methods of a power transmission line (PTL) cannot supply the demand for high quality and dependability for power grid maintenance. Consequently, the automatic PTL inspection technology becomes one of the key research focuses. For the purpose of summarizing related studies on environment perception and control technologies of PTL inspection, technologies of three-dimensional (3D) reconstruction, object detection, and visual servo of PTL inspection are reviewed, respectively. Firstly, 3D reconstruction of PTL inspection is reviewed and analyzed, especially for the technology of LiDAR-based reconstruction of power lines. Secondly, the technology of typical object detection, including pylons, insulators, and power line accessories, is classified as traditional and deep learning-based methods. After that, their merits and demerits are considered. Thirdly, the progress and issues of visual servo control of inspection robots are also concisely addressed. For improving the automation degree of PTL robots, current problems of key techniques, such as multisensor fusion and the establishment of datasets, are discussed and the prospect of inspection robots is presented.

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“…A typical method to count objects with a CNN is to train an object detection model and subsequently count the number of detected objects [13][14][15]. Because most object-detection neural networks are designed to detect typical everyday objects, they might provide inferior results on counting tasks where small and connected objects are involved.…”

Section: Introductionmentioning

confidence: 99%

CentroidNetV2: A hybrid deep neural network for small-object segmentation and counting

Dijkstra

Loosdrecht

Atsma³

et al. 2021

Neurocomputing

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Distribution Line Pole Detection and Counting Based on YOLO Using UAV Inspection Line Video

Cited by 65 publications

References 12 publications

3d Photogrammetric Inspection of Risers Using Rpas and Deep Learning in Oil and Gas Offshore Platforms

3d Photogrammetric Inspection of Risers Using Rpas and Deep Learning in Oil and Gas Offshore Platforms

Environment Perception Technologies for Power Transmission Line Inspection Robots

CentroidNetV2: A hybrid deep neural network for small-object segmentation and counting

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