基于CPLC-YOLOv8的轻量型绝缘子缺陷检测算法OA

[Objective]Aiming at the problems of small insulator defect size,susceptibility to complex background interference during detection,and large parameter volume of the baseline model,this paper proposes a lightweight insulator defect detection algorithm based on an improved CPLC-YOLOv8.[Methods]Firstly,the lightweight RepNCSPELAN4-CAA module was designed to replace the C2f module in YOLOv8's backbone network,reducing parameter quantity while enhancing feature representation capability.Secondly,a small-defect detection layer P2 was added to strengthen the fusion of shallow and deep features,minimizing the loss of small-target information.Subsequently,a lightweight detection head was developed,where 1×1 convolution was employed for channel dimension adjustment and detail-enhanced convolution was utilized to replace conventional 3×3 convolution,achieving parameter sharing and feature enhancement.Finally,the convolutional block attention mechanism was introduced to suppress background interference through dual channel-spatial attention mechanisms,enhancing key feature representation and improving model robustness and detection accuracy.[Results]Experimental results on the custom insulator defect dataset demonstrated that the proposed CPLC-YOLOv8 achieved a mAP@0.5 of 0.928,representing a 2 percentage point improvement over the original YOLOv8.The model parameters were reduced to only 1.72 MB(42.8%reduction compared to YOLOv8),with a compressed model size of 4.12 MB(31.3%compression).Comparative evaluations with classic network models confirmed that CPLC-YOLOv8 exhibited significant advantages in detection accuracy,parameter efficiency,and model compactness,particularly demonstrating superior robustness and generalization capability in small object detection tasks.[Conclusion]The proposed algorithm achieves lightweight model design while maintaining high detection accuracy,making it suitable for deployment on resource-constrained edge devices with promising engineering application prospects.Future work will further explore the integration of multi-scale feature fusion and lightweight techniques to continuously enhance the algorithm's adaptability and stability in practical power inspection scenarios.