Enhanced Defect Detection in Insulator Iron Caps Using Improved YOLOv8n
Journal Title: Information Dynamics and Applications - Year 2024, Vol 3, Issue 3
Abstract
To address the challenges in detecting surface defects on insulator iron caps, particularly due to the complex backgrounds that hinder accurate identification, an improved defect detection algorithm based on YOLOv8n, whose full name is You Only Look Once version 8 nano, was proposed. The C2f convolutional layers in both the backbone and neck networks were replaced by the C2f-Spatial and Channel Reconstruction Convolution (SCConv) convolutional network, which strengthens the model’s capacity to extract detailed surface defect features. Additionally, a Convolutional Block Attention Module (CBAM) was incorporated after the Spatial Pyramid Pooling - Fast (SPPF) layer, enhancing the extraction of deep feature information. Furthermore, the original feature fusion method in YOLOv8n was replaced with a Bidirectional Feature Pyramid Network (BiFPN), significantly improving the detection accuracy. Extensive experiments conducted on a self-constructed dataset demonstrated the effectiveness of this approach, with improvements of 2.7% and 2.9% in mAP@0.5 and mAP@0.95, respectively. The results confirm that the proposed algorithm exhibits strong robustness and superior performance in detecting insulator iron cap defects under varied conditions.
Authors and Affiliations
Qiming Zhang, Ying Liu, Song Tang, Kui Kang
Keywords
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- EP ID EP752497
- DOI https://doi.org/10.56578/ida030302
- Views 41
- Downloads 1
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