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    基于权系数改进YOLOv4损失函数的高精度表计光学图像识别

    Tabular Optical Image Recognition with Improved YOLOv4 Loss Function Based on Weight Coefficient

    • 摘要: 针对变电站智能运维背景下表计光学图像自主定位识别目标,在YOLOv4算法中LEIOU损失函数基础上,研究了基于权系数改进YOLOv4损失函数的高精度表计光学图像识别算法。从理论上分析了损失函数计算原理,并对损失函数重叠面积、中心位置欧氏距离、横纵比、置信度采用权系数改进。将算法应用于河北衡水某变电站内,结果表明,使用该权系数改进损失函数LWIOU后能够对表计实现正确定位及读数,初步证明该方法的有效性,相对LDIOULCIOULEIOU算法,经过权系数优化多次迭代后LWIOU算法体现出更佳的损失函数收敛效果,且相对目前效果最好的LEIOU,在精确率、召回率、平均精度解果分别提升了1.06%、0.64%、1.25%,在变电站表计监测领域具有一定工程意义。

       

      Abstract: Based on LEIOU loss function in YOLOv4 algorithm, a high-precision meter optical image recognition algorithm based on weight coefficient improvement of YOLOv4 loss function is studied in this paper, aiming at the self-localization and recognition targets of meter optical images under the background of substation intelligent operation and maintenance. The calculation principle of loss function is analyzed theoretically, and weight coefficient is used to improve the overlapping area of loss function, Euclidean distance of central position, horizontal and vertical ratio and confidence degree. The algorithm is applied to a substation in Hengshui, Hebei Province. The results show that the meter can be correctly located and read. After several iterations of weight coefficient optimization, compared with LDIOULCIOULEIOU algorithms, LWIOU algorithm shows better loss function convergence effect, and compared with LEIOU, which has the best effect at present, the accuracy rate, recall rate and average accuracy of the solution are increased by 1.06%, 0.64% and 1.25% respectively, which has certain engineering significance in the field of substation meter monitoring.

       

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