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Wang, C., Bochkovskiy, A. and Liao, H.M. (2023) YOLOv7: Trainable Bag-Of-Freebies Sets New State-Of-The-Art for Real-Time Object Detectors. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, 17-24 June 2023, 7464-7475.
https://doi.org/10.1109/cvpr52729.2023.00721

has been cited by the following article:

• TITLE: PE Gas Pipeline Defect Detection Method Based on YOLOv7 Algorithm

  AUTHORS: Junli Wang, Li Wang, Yang Wang

  KEYWORDS: Image Enhancement, Defect Detection, YOLOv7 Algorithm

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.15 No.9, September 1, 2025

  ABSTRACT: Regular pipeline inspections are crucial for timely identification of critical defects and ensuring pipeline integrity. To address the challenges of detecting defects in PE gas pipelines under low illumination and low-resolution conditions, this paper proposes a defect detection method based on the YOLOv7 algorithm. This approach significantly improves the accuracy of defect detection and classification.