Detection and Recognition of Digital Instruments Based on Lightweight YOLO-v4 Model at Substations
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摘要:
为了在变电站实际场景中准确获取数字仪表读数，智能管控变电站的安全风险，同时推动变电站智能化发展，以实际场景中变电站数字仪表作为研究对象，综合考虑实时性及准确度等，提出一种基于轻量级YOLO-v4模型的变电站数字仪表检测识别方法. 首先，通过从鄂尔多斯变电站实际拍摄变电站数字仪表图像数据，使用Albumentations框架对数字仪表图像进行数据扩充，构建变电站数字仪表目标检测数据集；然后，以YOLO-v4网络为基础，结合注意力机制构建一个有效通道注意（efficient channel attention，ECA）改进的深度可分离卷积模块（ECA-bneck-m）；最后，提出一个轻量级YOLO-v4模型，进行模型大小与性能的对比实验. 实验结果表明：本文方法可以在几乎不损失检测准确度的情况下，将整个模型存储大小压缩为原先的1/5，同时将模型推理速度从24.0帧/s提升至36.9帧/s，其实时性能够满足实际变电站检测识别的工程需要.
Abstract:In order to accurately recognize the readings of digital instruments in the actual scene of substations, intelligently control substation security, and promote its intelligent development, the digital instruments in the substation are taken as the research object, and in view of real-time and accuracy, a lightweight YOLO-v4 model is proposed for the detection and recognition of digital instruments. Firstly, the digital instrument images captured from the Ordos substation are expanded by using the Albumentations framework, thus building an effective digital instrument data set for detection and recognition. After that, an efficient channel attention (ECA)-based deep separable convolution block (ECA-bneck-m) is constructed with attention mechanism, and further a lightweight YOLO-v4 model is proposed to conduct comparative experiments on model size and performance. Finally, experiments comparing model size and performance are performed. The results show that, the storage size of the model can be compressed by about 5 times nearly without loss of detection accuracy, and the processing speed of model can be increased from 24.0 frame/s to 36.9 frame/s, indicating that the proposed model can meet the requirements of real-time detection and recognition in the actual substation.
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图 1深度学习数字仪表检测识别方法总体框架
Figure 1.General framework of deep learning-based method for detection and recognition of digital instruments
图 14数字仪表检测与读数识别结果示意
Figure 14.Recognition results of digital instrument detection and reading
表 1图像数据扩充结果
Table 1.Image data expansion results
幅 数据集 数字仪表 数字字符 总计 原数据集 1571 1201 2772 扩充数据集 5000 5000 10000 
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表 2k-means预选框聚类结果
Table 2.k-means clustering results of prior box
模型 特征层 13 × 13 26 × 26 52 × 52 仪表检测
模型（204, 149） （84, 174） （5, 16） （221, 448） （128, 227） （21, 36） （288, 144） （174, 479） （71, 131） 字符识别
模型（159, 191） （94, 127） （14, 24） （163, 270） （127, 167） （42, 62） （297, 876） （131, 633） （70, 308） 下载:
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表 3SPP层不同池化尺度性能对比结果
Table 3.Performance comparison of SPP layer at different pooling scales
池化尺度 mAP/% {3 × 3, 5 × 5} 99.75 {5 × 5, 7 × 7} 99.69 {7 × 7, 9 × 9} 99.78 {7 × 7, 11 × 11} 99.74 {5 × 5, 9 × 9} 99.68 下载:
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表 4不同网络模型大小对比结果
Table 4.Comparison results of different model sizes
网络模型 参数量/个 模型大小/MB YOLO-v4 （DarkNet-53） 63986151 244.0 YOLO-v4 （bneck） 14018719 53.8 YOLO-v4 （bneck-m） 14018719 53.8 YOLO-v4 （ECA-bneck-m） 12506463 48.0 下载:
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表 5不同深度学习目标检测模型对比结果
Table 5.Comparison of different deep learning detection models
网络 mAP/% FPS/（帧·s−1） Faster-RCNN 83.88 6.0 YOLO-v3 99.64 30.0 YOLO-v4 99.80 24.0 轻量级YOLO-v4 （bneck） 99.58 33.7 轻量级YOLO-v4 （bneck-m） 99.75 35.6 轻量级YOLO-v4 （ECA-bneck-m） 99.78 36.9 下载:
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表 6轻量级YOLO-v4（ECA-bneck-m）测试结果
Table 6.Lightweight YOLO-v4 （ECA-bneck-m） test results
类别 P/% R/% F1 字符 0 识别 99.83 99.65 1.00 字符 1 识别 99.43 98.87 0.99 字符 2 识别 98.58 100.00 0.99 字符 3 识别 100.00 100.00 1.00 字符 4 识别 98.56 100.00 0.99 字符 5 识别 96.58 99.30 0.98 字符 6 识别 95.27 98.60 0.97 字符 7 识别 99.05 100.00 1.00 字符 8 识别 97.54 100.00 0.99 字符 9 识别 100.00 99.25 1.00 仪表检测 97.22 100.00 0.99 数显区域定位 98.25 100.00 0.99 下载:
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