InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images-Reference-Cited by-同舟云学术

InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images

Published:2023-11-27 Issue:23 Volume:44 Page:7294-7320
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Vieira e Silva André Luiz Buarque¹,de Castro Felix Heitor¹,Simões Franscisco Paulo Magalhães¹²,Teichrieb Veronica¹,dos Santos Michel³,Santiago Hemir⁴,Sgotti Virginia³,Lott Neto Henrique⁵

Affiliation:

1. Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil

2. Visual Computing Lab, Departamento de Computação, Universidade Federal Rural de Pernambuco, Recife, Brazil

3. In Forma Software,Universidade Federal de Pernambuco Centro de Informatica, Recife, Brazil

4. Escola Politécnica de Pernambuco, Universidade de Pernambuco, Recife, Brazil

5. Sistema de Transmissão Nordeste,Universidade Federal de Pernambuco Centro de Informatica, Recife, Brazil

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2283900

Reference59 articles.

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2. Bergmann, P., M. Fauser, D. Sattlegger, and C. Steger 2019, June. MVTec AD – a Comprehensive Real-World Dataset for Unsupervised Anomaly Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.

3. Bergmann, P., S. Lãwe, M. Fauser, D. Sattlegger, and C. Steger. (2019). Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, pp. 372–380. INSTICC: SciTePress.

4. A monocular vision–based perception approach for unmanned aerial vehicle close proximity transmission tower inspection

5. YOLACT: Real-Time Instance Segmentation