PLAD: A Dataset for Multi-Size Power Line Assets Detection in
High-Resolution UAV Images
- URL: http://arxiv.org/abs/2108.07944v1
- Date: Wed, 18 Aug 2021 02:26:05 GMT
- Title: PLAD: A Dataset for Multi-Size Power Line Assets Detection in
High-Resolution UAV Images
- Authors: Andr\'e Luiz Buarque Vieira-e-Silva, Heitor de Castro Felix, Thiago de
Menezes Chaves, Francisco Paulo Magalh\~aes Sim\~oes, Veronica Teichrieb,
Michel Mozinho dos Santos, Hemir da Cunha Santiago, Virginia Ad\'elia
Cordeiro Sgotti, Henrique Baptista Duffles Teixeira Lott Neto
- Abstract summary: This work proposes the Power Line Assets dataset, containing high-resolution and real-world images of power line components.
It has 2,409 annotated objects divided into five classes: transmission tower, insulator, spacers, tower plate, and Stockbridge damper.
It also presents an evaluation with popular deep object detection methods, showing considerable room for improvement.
- Score: 1.4698569949278106
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Many power line companies are using UAVs to perform their inspection
processes instead of putting their workers at risk by making them climb high
voltage power line towers, for instance. A crucial task for the inspection is
to detect and classify assets in the power transmission lines. However, public
data related to power line assets are scarce, preventing a faster evolution of
this area. This work proposes the Power Line Assets Dataset, containing
high-resolution and real-world images of multiple high-voltage power line
components. It has 2,409 annotated objects divided into five classes:
transmission tower, insulator, spacer, tower plate, and Stockbridge damper,
which vary in size (resolution), orientation, illumination, angulation, and
background. This work also presents an evaluation with popular deep object
detection methods, showing considerable room for improvement. The PLAD dataset
is publicly available at https://github.com/andreluizbvs/PLAD.
Related papers
- InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV
Images [1.8524180288472398]
This paper introduces InsPLAD, a Power Line Asset Inspection dataset and Benchmark containing 10,607 high-resolution Unmanned Aerial Vehicles colour images.
The dataset contains seventeen unique power line assets captured from real-world operating power lines.
We thoroughly evaluate state-of-the-art and popular methods for three image-level computer vision tasks covered by InsPLAD: object detection, through the AP metric; defect classification, through Balanced Accuracy; and anomaly detection, through the AUROC metric.
arXiv Detail & Related papers (2023-11-02T22:06:23Z) - Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart
Grid [56.838297900091426]
An unsupervised Machine Learning (ML) framework is proposed, to detect, extract and analyze the characteristics of power lines of both high and low voltage.
The proposed framework can efficiently detect the power lines and perform PLC-based hazard analysis.
arXiv Detail & Related papers (2023-08-14T17:14:58Z) - DUFormer: Solving Power Line Detection Task in Aerial Images using
Semantic Segmentation [17.77548837421917]
Unmanned aerial vehicles (UAVs) are frequently used for inspecting power lines and capturing high-resolution aerial images.
To tackle this problem, we introduce DUFormer, a semantic segmentation algorithm explicitly designed to detect power lines in aerial images.
Our proposed method outperforms all state-of-the-art methods in power line segmentation on the publicly accessible TTPLA dataset.
arXiv Detail & Related papers (2023-04-12T12:59:02Z) - Hierarchical Point Attention for Indoor 3D Object Detection [111.04397308495618]
This work proposes two novel attention operations as generic hierarchical designs for point-based transformer detectors.
First, we propose Multi-Scale Attention (MS-A) that builds multi-scale tokens from a single-scale input feature to enable more fine-grained feature learning.
Second, we propose Size-Adaptive Local Attention (Local-A) with adaptive attention regions for localized feature aggregation within bounding box proposals.
arXiv Detail & Related papers (2023-01-06T18:52:12Z) - DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients [105.25109274550607]
Line segments are increasingly used in vision tasks.
Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions.
We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector.
arXiv Detail & Related papers (2022-12-15T12:36:49Z) - Transformers for Object Detection in Large Point Clouds [9.287964414592826]
We present TransLPC, a novel detection model for large point clouds based on a transformer architecture.
We propose a novel query refinement technique to improve detection accuracy, while retaining a memory-friendly number of transformer decoder queries.
This simple technique has a significant effect on detection accuracy, which is evaluated on the challenging nuScenes dataset on real-world lidar data.
arXiv Detail & Related papers (2022-09-30T06:35:43Z) - An Extendable, Efficient and Effective Transformer-based Object Detector [95.06044204961009]
We integrate Vision and Detection Transformers (ViDT) to construct an effective and efficient object detector.
ViDT introduces a reconfigured attention module to extend the recent Swin Transformer to be a standalone object detector.
We extend it to ViDT+ to support joint-task learning for object detection and instance segmentation.
arXiv Detail & Related papers (2022-04-17T09:27:45Z) - Embracing Single Stride 3D Object Detector with Sparse Transformer [63.179720817019096]
In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases.
Many 3D detectors directly follow the common practice of 2D detectors, which downsample the feature maps even after quantizing the point clouds.
We propose Single-stride Sparse Transformer (SST) to maintain the original resolution from the beginning to the end of the network.
arXiv Detail & Related papers (2021-12-13T02:12:02Z) - Perceiving Traffic from Aerial Images [86.994032967469]
We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images.
We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
arXiv Detail & Related papers (2020-09-16T11:37:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.