Chai Yujiao, Yao Xiaomin, Chen Manlong, Shan Sirui
School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723000, China.
Sensors (Basel). 2025 Jul 4;25(13):4165. doi: 10.3390/s25134165.
The timely detection of insulator defects in transmission lines is vital for ensuring social production and people's livelihoods. Aiming to solve the problem of the low accuracy of insulator defect detection in current detection models, this study improves the YOLO11n model and proposes an insulator defect detection model, FPFS-YOLO, that integrates FasterNet and an attention mechanism. In this study, to mitigate parameter redundancy in the backbone of the YOLO11n model, the FasterNet lightweight network was introduced, and some convolution was embedded into the shallow network to enhance its feature extraction ability. To solve problems such as insufficient attention to important features and the low detection ability of small defects in the YOLO11n model network, the ParNet attention mechanism was added, along with a small-defect detection layer, which improved the detection accuracy of the model. Finally, in order to alleviate the computational redundancy caused by these additions, the C3k2_faster module and the PSP-Head detection head were introduced. These amendments further improved the accuracy of the model network in detecting insulator defects while simultaneously reducing its computational redundancy. The experimental results show that the improved FPFS-YOLO model achieved a 91.5% mAP@50 and a 56.6% mAP@0.5-0.95, increases of 3.1% and 1.2%, respectively, while the precision and recall reached 93.2% and 86.4%, increases of 1.5% and 4.2%, respectively. The FPFS-YOLO model achieved a higher detection accuracy than the YOLO11n model and thus could be widely applied in the detection of insulator defects.
及时检测输电线路中的绝缘子缺陷对于保障社会生产和人民生活至关重要。为了解决当前检测模型中绝缘子缺陷检测准确率低的问题，本研究对YOLO11n模型进行了改进，提出了一种集成FasterNet和注意力机制的绝缘子缺陷检测模型FPFS - YOLO。在本研究中，为了减轻YOLO11n模型主干中的参数冗余，引入了FasterNet轻量级网络，并在浅层网络中嵌入了一些卷积以增强其特征提取能力。为了解决YOLO11n模型网络中对重要特征关注不足以及小缺陷检测能力低等问题，添加了ParNet注意力机制以及一个小缺陷检测层，提高了模型的检测准确率。最后，为了缓解这些添加所导致的计算冗余，引入了C3k2_faster模块和PSP - Head检测头。这些改进进一步提高了模型网络检测绝缘子缺陷的准确率，同时降低了其计算冗余。实验结果表明，改进后的FPFS - YOLO模型的mAP@50达到了91.5%，mAP@0.5 - 0.95达到了56.6%，分别提高了3.1%和1.2%，而精确率和召回率分别达到了93.2%和86.4%，分别提高了1.5%和4.2%。FPFS - YOLO模型比YOLO11n模型具有更高的检测准确率，因此可广泛应用于绝缘子缺陷检测。
