An Improved Mask2Former-HRNet Method for Insulator Defect Detection
2025
Yaoran Huo | Lan Xiao | Zhenyu Tang | Jian Zhou | Xu Dai | Yuhao Xiao | Xia Fang
To solve the problem of scale variation in insulator images captured by drones, caused by the lack of control over angle and distance, which makes it hard to detect subtle defects, this paper proposes an instance segmentation method based on an improved Mask2Former-HRNet model for precise localization and defect detection of transmission line insulators. First, a mask-guided and matching component is added to Mask2Former to reduce the misjudgment rate of insulator defects by including noisy label masks. Second, the HRNet backbone network is used to better capture the spatial and shape information of insulators, as it has a stronger feature transfer ability. Deformable convolutions are introduced to handle deformation issues caused by varying angles in insulator images. Then, an attention mechanism is added to focus on key content, improving the network&rsquo:s attention to crucial information. Finally, experimental results on defect detection of transmission line insulator images captured by drones show that the proposed method increases the detection accuracy by 8.41% and reduces the misjudgment rate by 4.11%. Comparative experiments indicate that the proposed method outperforms existing methods in several evaluation metrics.
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