Abstract
YOLO target detection algorithm is the mainstream method for image-based defect detection of transmission line insulators. However, the existing model complexity is large. A reasonable and effective parameter compression method is urgently needed as a prerequisite to lay the foundation for solving the dilemma of UAV edge equipment deployment. At the same time, the insulator defect images taken by UAVs have complex backgrounds and small defect sizes, which are prone to misdetection and omission. It is needed to improve the channel domain of the CBAM (convolution block attention module) attention mechanism and solve its problem of missing channel information due to dimensionality reduction. The improved CBAM is added to the backbone network of YOLOv5s. The goal is to enable the model to identify and pinpoint the critical objectives with greater precision. The model is combined with Cross Entropy Loss and Lovasz-Softmax Loss weighting, which makes the network converge more stably during the training process, and the accuracy rate is also somewhat improved.
Export citation and abstract BibTeX RIS
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.