计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 412-417.doi: 10.11896/jsjkx.210600089
马宾, 付永康, 王春鹏, 李健, 王玉立
MA Bin, FU Yong-kang, WANG Chun-peng, LI Jian, WANG Yu-li
摘要： 绝缘子检测是保障输电系统安全稳定的重要措施,绝缘子定位是进行检测的前提。针对目前电力巡检中绝缘子定位速度慢、精度低的问题,提出了一种基于GDIoU(Gaussian Distance Intersection over Union)损失函数的YOLOv4深度学习框架。该方案通过设计GDIoU损失函数来提高YOLOv4的定位精度和收敛速度,利用二维高斯模型提高了网络的收敛能力,增强了YOLOv4的性能,进而提高了绝缘子的定位精度与速度。同时提出绝缘子自适应旋转矫正算法,通过对单个绝缘子图像进行旋转矫正,提升了在不同空间状态下的绝缘子识别精度。实验结果表明,与朴素YOLOv4相比,所提算法的定位精度提高了7.37%。在同水平的精度下,基于GDIoU的YOLOv4绝缘子定位方法比其他绝缘子定位算法速度快了3倍以上。所提方法在精度与速度上做了较好的平衡,其性能完全满足电力巡检中绝缘子的在线定位要求。
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