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BEAD-Net: Bidirectional Fusion and Hourglass Expanded Asymmetric Detection for Insulator Defect Detection
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DOI：10.3390/electronics15163580.png)
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Abstract
En 中文
UAV-based insulator defect detection faces persistent challenges of multi-scale defect variation, background clutter, small-target missed detections and redundant detection-head computation. This paper proposes BEAD-Net, a real-time insulator defect detection network built upon YOLOv11n with four targeted improvements. First, an Hourglass Symmetric Residual Attention (HSRA) module replaces the standard bottleneck components within C3k2, expanding the multi-scale receptive field and suppressing background interference via a symmetric hourglass dilation schedule and channel attention recalibration. Second, a Bidirectional Diffusion Feature Pyramid Network (BDFPN) built upon the Group-wise Selective Feature Integrator (GSFI) employs group-wise adaptive gating and two-level bidirectional propagation to mitigate semantic dilution during cross-scale fusion. Third, a Task-Decoupled Asymmetric Detection Head (TDAH) concentrates spatial modeling in the regression branch while simplifying classification to lightweight 1 × 1 operations, reducing parameter redundancy and alleviating inter-task gradient conflict. Finally, Focaler-PIoU2 integrates linear interval mapping with a normalized corner distance penalty to improve boundary regression for slender insulator structures. On the IDID dataset, BEAD-Net achieves mAP@0.5 of 83.98% and mAP@0.5:0.95 of 63.82% at 275.04 FPS with 2.49 M parameters, outperforming the baseline YOLOv11n by 2.83 and 1.55 percentage points and surpassing all compared state-of-the-art methods. Additional validation on the CPLID benchmark shows that BEAD-Net remains the top performer, confirming that the proposed improvements are not specific to a single dataset.
Keywords:
defect detection
YOLOv11n
insulator
attention mechanism
multi-scale feature fusion
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