An Improved YOLOv5 Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images

Zhenhui Sun, Peihang Li, Qingyan Meng, Yunxiao Sun, Yaxin Bi, Melanie Vanderhoof (Editor)

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
56 Downloads (Pure)

Abstract

Tailings ponds’ failure and environmental pollution make tailings monitoring very important. Remote sensing technology can quickly and widely obtain ground information and has become one of the important means of tailings monitoring. However, the efficiency and accuracy of traditional remote sensing monitoring technology have difficulty meeting the management needs. At the same time, affected by factors such as the geographical environment and imaging conditions, tailings have various manifestations in remote sensing images, which all bring challenges to the accurate acquisition of tailings information in large areas. By improving You Only Look Once (YOLO) v5s, this study designs a deep learning-based framework for the large-scale extraction of tailings ponds information from the entire high-resolution remote sensing images. For the improved YOLOv5s, the Swin Transformer is integrated to build the Swin-T backbone, the Fusion Block of efficient Reparameterized Generalized Feature Pyramid Network (RepGFPN) in DAMO-YOLO is introduced to form the RepGFPN Neck, and the head is replaced with Decoupled Head. In addition, sample boosting strategy (SBS) and global non-maximum suppression (GNMS) are designed to improve the sample quality and suppress repeated detection frames in the entire image, respectively. The model test results based on entire Gaofen-6 (GF-6) high-resolution remote sensing images show that the F1 score of tailings ponds is significantly improved by 12.22% compared with YOLOv5, reaching 81.90%. On the basis of both employing SBS, the improved YOLOv5s boots the [email protected] of YOLOv5s by 5.95%, reaching 92.15%. This study provides a solution for tailings ponds’ monitoring and ecological environment management.
Original languageEnglish
Article number1796
Pages (from-to)1-21
Number of pages22
Journalremote sensing
Volume15
Issue number7
Early online date28 Mar 2023
DOIs
Publication statusPublished online - 28 Mar 2023

Bibliographical note

Funding Information:
This research was funded by the Tianjin Municipal Education Commission Scientific Research Program (grant number 2021SK003); the Tianjin Educational Science Planning Project (grant number EHE210290); the Tianjin outstanding science and Technology Commissioner project (grant number 22YDTPJC00230); the National Natural Science Foundation of China (grant number 41971310); National Natural Science Foundation of China (grant number 42171357); National Key Research and Development Program of China (grant number YFC3301602).

Publisher Copyright:
© 2023 by the authors.

Keywords

  • Article
  • tailings ponds
  • YOLOv5
  • object detection
  • large scale

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