College of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel). 2023 Jun 1;23(11):5249. doi: 10.3390/s23115249.
Insulator defect detection is of great significance to compromise the stability of the power transmission line. The state-of-the-art object detection network, YOLOv5, has been widely used in insulator and defect detection. However, the YOLOv5 network has limitations such as poor detection rate and high computational loads in detecting small insulator defects. To solve these problems, we proposed a light-weight network for insulator and defect detection. In this network, we introduced the Ghost module into the YOLOv5 backbone and neck to reduce the parameters and model size to enhance the performance of unmanned aerial vehicles (UAVs). Besides, we added small object detection anchors and layers for small defect detection. In addition, we optimized the backbone of YOLOv5 by applying convolutional block attention modules (CBAM) to focus on critical information for insulator and defect detection and suppress uncritical information. The experiment result shows the mean average precision (mAP) is set to 0.5, and the mAP is set from 0.5 to 0.95 of our model and can reach 99.4% and 91.7%; the parameters and model size were reduced to 3,807,372 and 8.79 M, which can be easily deployed to embedded devices such as UAVs. Moreover, the speed of detection can reach 10.9 ms/image, which can meet the real-time detection requirement.
绝缘子缺陷检测对于保证输电线路的稳定性具有重要意义。目前的目标检测网络 YOLOv5 已经广泛应用于绝缘子和缺陷检测。然而，YOLOv5 网络在检测小绝缘子缺陷时存在检测率差、计算负载高等局限性。为了解决这些问题，我们提出了一种用于绝缘子和缺陷检测的轻量级网络。在这个网络中，我们将 Ghost 模块引入 YOLOv5 骨干网和颈部，以减少参数和模型大小，从而提高无人机（UAV）的性能。此外，我们添加了小目标检测锚和层，用于检测小缺陷。此外，我们通过应用卷积块注意模块（CBAM）对 YOLOv5 的骨干进行了优化，以关注绝缘子和缺陷检测的关键信息，并抑制不重要的信息。实验结果表明，我们的模型在 mAP 设置为 0.5 时，mAP 设置为 0.5 到 0.95 时可以达到 99.4%和 91.7%；参数和模型大小减少到 3807372 和 8.79M，可以轻松部署到无人机等嵌入式设备上。此外，检测速度可以达到 10.9ms/image，可以满足实时检测的要求。
