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摘要:
极性电子元器件的类别、方向识别、定位在工业生产、焊接和检测等领域发挥着至关重要的作用. 本文首先将极性电子元器件的方向识别问题转化为一个分类问题, 然后, 采用Faster RCNN (Region convolutional neural network) 与YOLOv3方法实现了极性电子元器件的准确分类、方向识别和精准定位. 实验取得良好的效果, 两种算法的平均准确率(Mean average precision, mAP) 分别达到97.05 %、99.22 %. 此外, 我们通过数据集目标框的长宽分布, 利用K-means算法对Faster RCNN和YOLOv3的Anchor boxes进行了改进设计, 使准确率分别提高了1.16 %、0.1 %, 并提出针对大目标检测的网络结构: YOLOv3-BigObject, 在提高准确率的同时, 将检测单张图片的时间大幅缩减为原来检测时间的一半, 并最终用焊接有元器件的电路板进行检测, 得到了很好的实验结果.
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关键词:
- 电子制造 /
- 深度学习 /
- 方向识别 /
- 目标检测 /
- Faster RCNN
Abstract:The category, direction identification and positioning of polar electronic components play an important role in the industrial production, welding and inspection. In this paper, we first successfully transform the original problem of directional identification of polar electronic components into a classification problem. Then, the Faster RCNN (region convolutional neural network) and YOLOv3 methods are used to realize the correct classification, direction identification and accurate positioning of the polar electronic components. The experiments validate the effectiveness of our proposed method and the mAP (mean average precision) of the two proposed algorithms can reach 97.05 %, 99.22 %. In addition, we improve the anchor boxes of the Faster RCNN and YOLOv3 by K-means algorithm through the length and width distributions of the target frames of the datasets, the accuracy can be improved by 1.16 %, 0.1 %. We also propose the YOLOv3-BigObject network structure for the large target detection, while improving the accuracy, the cost time for detecting a single picture is also greatly reduced. Finally, the board with the electronic components is chosen to test and good experimental results are obtained.
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Key words:
- Electronic manufacturing /
- deep learning /
- direction recognition /
- object detection /
- Faster RCNN
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图 1 基于传统和深度学习图像分类方法模型
Fig. 1 Traditional and deep learning image classification method based on model
图 5 基于Faster RCNN的Anchor boxes和训练集的分布
Fig. 5 Distribution of training set and anchors boxes based on Faster RCNN
图 6 基于YOLOv3的Anchor boxes和训练集的分布
Fig. 6 Distribution of training set and anchor boxes based on YOLOv3
图 9 YOLOv3-BigObject的内部网络参数设置结构
Fig. 9 The Internal network parameter setting structure of the YOLO v3-BigObject
图 11 Faster RCNN (ResNet101)的Loss曲线
Fig. 11 The loss curves of Faster RCNN (ResNet101)
图 12 YOLOv3和YOLOv3-BigObject的Loss曲线
Fig. 12 The loss curves of YOLOv3 and YOLOv3-BigObject
表 1 数据集1包含各类的图片数量和目标数量
Table 1 The dataset 1 contains the number of images and targets of targets in each category
二极管型号 方向1 (上) 方向2 (右) 方向3 (下) 方向4 (左) 总计 型号1 2 775 (2 934) 3 129 (3 300) 2 739 (2 899) 3 264 (3 460) 9 793 (12 593) 型号2 2 833 (2 970) 3 123 (3 315) 2 813 (2 946) 3 180 (3 369) 9 800 (12 600) 型号3 2 840 (2 993) 3 090 (3 309) 2 827 (2 977) 3 108 (3 321) 9 800 (12 600) 总计 7 032 (8 897) 7 570 (9 924) 6 975 (8 822) 7 676 (10 150) 14 000 (37 793) 
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表 2 数据集2包含各类的图片数量和目标数量
Table 2 The dataset 2 contains the number of images and targets of targets in each category
二极管型号 方向1 (上) 方向2 (右) 方向3 (下) 方向4 (左) 总计 型号1 1 278 (1 365) 1 349 (1 448) 1 322 (1 461) 1 329 (1 441) 4 949 (5 714) 型号2 1 282 (1 380) 1 380 (1 467) 1 307 (1 400) 1 344 (1 430) 4 970 (5 677) 总计 2 155 (2 745) 2 289 (2 915) 2 214 (2 861) 2 673 (2 871) 5 691 (11 391)
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表 3 数据集1的实验结果
Table 3 Experimental result of dataset 1
Backbone Anchor boxes Anchor boxes与训练集的平均IoU mAP (%) 测试速度(s) Faster RCNN VGG-16 9 0.7037 93.77 - Faster RCNN VGG-16 6 0.8577 94.95 - Faster RCNN ResNet101 9 0.7037 97.05 - Faster RCNN ResNet101 6 0.8577 97.36 - YOLOv3 Darknet-53 9 0.5728 99.22 - YOLOv3 Darknet-53 3 0.7362 99.31 0.0217 YOLOv3-BigObject - 3 0.7362 99.44 0.0118
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表 4 数据集1和数据集2的实验结果
Table 4 Experimental result of datasets 1 and 2
Backbone Anchor boxes Anchor boxes与训练集的平均IoU mAP (%) 测试速度(s) Faster RCNN ResNet101 9 0.6294 97.28 - Faster RCNN ResNet101 6 0.8000 97.53 - FPN ResNet101 9+ - 98.70 - SSD ResNet101 9+ - 76.25 - YOLOv3 Darknet-53 9 0.4476 99.26 - YOLOv3-BigObject - 3 0.6645 99.20 0.0118
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