Chen Yanping, Deng Chong, Sun Qiang, Wu Zhize, Zou Le, Zhang Guanhong, Li Wenbo
School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.
Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230001, China.
Sensors (Basel). 2024 Jan 3;24(1):290. doi: 10.3390/s24010290.
The accurate and efficient detection of defective insulators is an essential prerequisite for ensuring the safety of the power grid in the new generation of intelligent electrical system inspections. Currently, traditional object detection algorithms for detecting defective insulators in images face issues such as excessive parameter size, low accuracy, and slow detection speed. To address the aforementioned issues, this article proposes an insulator defect detection model based on the lightweight Faster R-CNN (Faster Region-based Convolutional Network) model (Faster R-CNN-tiny). First, the Faster R-CNN model's backbone network is turned into a lightweight version of it by substituting EfficientNet for ResNet (Residual Network), greatly decreasing the model parameters while increasing its detection accuracy. The second step is to employ a feature pyramid to build feature maps with various resolutions for feature fusion, which enables the detection of objects at various scales. In addition, replacing ordinary convolutions in the network model with more efficient depth-wise separable convolutions increases detection speed while slightly reducing network detection accuracy. Transfer learning is introduced, and a training method involving freezing and unfreezing the model is employed to enhance the network's ability to detect small target defects. The proposed model is validated using the insulator self-exploding defect dataset. The experimental results show that Faster R-CNN-tiny significantly outperforms the Faster R-CNN (ResNet) model in terms of mean average precision (mAP), frames per second (FPS), and number of parameters.
在新一代智能电气系统检测中，准确高效地检测出绝缘子缺陷是确保电网安全的重要前提。目前，用于检测图像中绝缘子缺陷的传统目标检测算法存在参数规模过大、准确率低、检测速度慢等问题。为解决上述问题，本文提出了一种基于轻量级Faster R-CNN（基于区域的卷积神经网络）模型（Faster R-CNN-tiny）的绝缘子缺陷检测模型。首先，通过用EfficientNet替换ResNet（残差网络）将Faster R-CNN模型的主干网络转变为其轻量级版本，在提高检测准确率的同时大幅减少模型参数。第二步是采用特征金字塔构建具有不同分辨率的特征图进行特征融合，从而能够检测不同尺度的物体。此外，用更高效的深度可分离卷积替换网络模型中的普通卷积，在略微降低网络检测准确率的同时提高了检测速度。引入迁移学习，并采用一种涉及冻结和解冻模型的训练方法来增强网络检测小目标缺陷的能力。使用绝缘子自爆缺陷数据集对所提出的模型进行验证。实验结果表明，Faster R-CNN-tiny在平均精度均值（mAP）、每秒帧数（FPS）和参数数量方面明显优于Faster R-CNN（ResNet）模型。
