深度學習應用在不同情況條碼定位之研究

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姓名

黃千祐(QIAN-YOU Huang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

深度學習應用在不同情況條碼定位之研究
(An Application of a Deep Learning Medthod for Barcode Localization under different environment)

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摘要(中)

隨著貿易越來越頻繁，每一個商品在製造、買賣的過程中都需要透過條碼來確保過程正確無誤，但條碼容易受光照不均、雜訊等因素影響，再加上條碼的種類十分多元，也讓條碼的定位更加困難。由於傳統方法只能找單一種類條碼且易受到雜訊的影響而使結果有誤，因此為了有效解決這些問題，本研究採用以深度學習為基礎的方法來研究多種類條碼的定位。本研究分為三個階段。第一階段訓練深度學習網路來找出條碼。考慮到FasterRCNN網路相較於RCNN等較舊的神經網路，其在物體辨識的領域中取得了更快更準的辨識結果，因此本研究採用FasterRCNN的架構進行神經網路的訓練。訓練所需資料採用CipherLab之條碼掃描器拍攝，總共包含10008張圖片，並以8:1:1比例分為訓練資料、驗證資料及測試資料。第二階段為提出2個有效的傳統影像處理演算法來辨識條碼。本研究先利用神經網路剪枝的方式試圖找出條碼的特徵，然而效果不佳，因此改採用Gradcam將神經網路的權重可視化方法，以獲得每層神經網路權重分別對圖片的哪些區域影響最大。藉此來幫助傳統影像處理演算法的優化。結果評估所採用的影像以CipherLab公司所提供之測試圖像作為依據，評估方法以辨識結果之precision及recall為主，其中又特別著重在recall值，本研究最終神經網路結果recall為0.89、precision為0.65，最終2種傳統影像方法模擬較佳者可達到recall數值為0.88、precision數值為0.52，顯示本研究對條碼辨識能夠有不錯的效果。第三階段會將傳統影像演算法較佳的結果實現在PDA模擬器上，其辨識速度約為1200ms。雖然在執行速度上有所不足，但本方法能夠成功的定位多種不同的條碼，並且擁有不錯的定位結果。

摘要(英)

Abstract
As trade grows more and more frequently, every product needs to use a barcode to ensure that the process of manufacturing and shipping is correct. However, the recognition of barcodes can easily be affected by factors such as uneven lighting and noise. In addition, the numerous categories of barcodes also make the barcode localization difficult. Traditional methods are limited for the detection capability with different types of barcodes and for the detection accuracy under noisy conditions. In order to solve these problems, we apply deep learning to localize various types of barcodes.

This research consists of three parts. Firstly, a deep learning neural network was trained to find out barcodes. FasterRCNN was adopted in this research because of its faster and more accurate detection results compared to the older neural network such as RCNN. The training data used were captured by the barcode scanner made by CipherLab. A total of 10008 images were divided into the training data, the verification data and the test data by a ratio of 8:1:1 with the verification and the test date in a ratio.

Secondly, this research proposed two algorithms based on the results of FasterRCNN. Neural network pruning was used to find the feature of barcodes. However, the result was unsatisfactory. Hence, a weight visualization method, Gradcam, was used to get the saliency maps at some layers of the neural network. By using the result of Gradcam, two traditional image processing approaches was proposed for barcode detection. Method 1 used the image entropy to enhance the region of barcodes. Method 2 detected barcodes by morphology. Testing barcode images provided by CipherLab were used to validate the performance of the two methods. We used recall and precision for our evaluation method, and focused on the performance of recall.

The recall value of FasterRCNN was 0.89 and precision value was 0.65. Method 1 obtained the recall value of 0.86 and the precision value of 0.53, while Method 2 was with the recall value of 0.88 and the precision value of 0.52. The result showed that our method can detection barcode well.

Finally, we choose method 2 and implemented it on the PDA simulator. On PDA simulator we can detect all the barcodes of an image within 1.2 seconds. Although we do not have very good barcode detection speed, our method can successfully detect various kind of barcodes and achieve good detection results.

關鍵字(中)

★ 深度學習
★ 條碼
★ 物體定位

關鍵字(英)

★ Deep Learning
★ Barcode
★ Object Localization

論文目次

目錄
摘要 2
Abstract 4
目錄 I
表目錄 IX
第一章緒論 1
1.1研究動機: 1
1.2 基本條碼介紹: 2
1.3 文獻探討: 5
1.3.1傳統條碼定位方法 5
1.3.2類神經條碼定位方法: 9
1.4 研究目的: 12
1.5 論文架構: 13
第二章神經網路簡述 15
2.1 類神經網路概述 15
2.2類神經網路 18
2.2.1感知器(Perception) 18
2.2.2激活函數(Activation Function) 19
2.3.1 卷積神經網路(Convolutional Neural Networks) 20
2.3.2 ResNet殘差網路(Residual Networks) 23
2.4 FasterRCNN深度學習網路 24
2.4.1 Backbone網路 26
2.4.2 RPN網路 27
2.4.3 ROI Pooling(Region of Interest Pooling) 31
2.4.4 Classifier 33
第三章 FasterRCNN條碼定位結果及模擬 34
3.1軟硬體規格影像及數據準備介紹 34
3.1.1 軟硬體規格 34
3.1.2 數據準備介紹 35
3.2神經網路剪枝 41
3.3 深度神經網路之Gradcam結果 44
3.4 傳統常用之影像演算法介紹 48
3.4.1高斯函數與高斯濾波器(Gaussian Filter) 48
3.4.2影像模糊化(Image Blur) 49
3.4.3 形態學影像處理 50
3.4.4 大津二值化(Otsu Binarization) 53
3.4.5中值濾波(Median Filter) 54
3.4.6圖像熵(Image Entropy) 55
3.5神經網路模擬演算法 56
3.5.1演算法架構1 56
3.5.2演算法架構2 61
第四章研究結果與評估 67
4.1 傳統影像演算法分割結果與評估 67
4.1.1 演算法架構1之條碼定位結果 67
4.1.2 演算法架構2之條碼定位結果 69
4.2 WinCE模擬器介紹及結果 71
4.2.1 WinCE模擬器使用者介面介紹 71
4.2.2 WinCE模擬器定位條碼的最終結果 72
第五章結論與未來展望 78
5.1結論 78
5.2 未來展望 79
參考文獻 81

參考文獻

參考文獻
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Andy的條碼世界 (2012)。2021 年 1 月 7 日取自http://www.appsbarcode.com/barcode-type.php
http://www.appsbarcode.com/Code%2039.php

指導教授

吳炤民(Chao-Min W)

審核日期

2021-1-26

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