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研究生:蔣國祥
研究生(外文):Kuo-HsiangChiang
論文名稱:以FPGA為基礎實現且應用於機械手臂之影像辨識系統
論文名稱(外文):An Image Recognition System Implementation Based on FPGA Applied to Robot Arm
指導教授:羅錦興羅錦興引用關係
指導教授(外文):Ching-Hsing Luo
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電機工程學系專班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:94
中文關鍵詞:影像辨識顏色檢測FPGA
外文關鍵詞:Image RecognitionColor DetectionFPGA
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一套影像辨識系統需要龐大且複雜的系統資源,包含大量的記憶體、快速的處理器運算能力、影像擷取裝置及顯示裝置,目前的個人電腦技術普遍具備這些資源及裝置,足以應付複雜的影像辨識系統,因此目前大多數的影像辨識技術是在電腦 (計算機) 系統上執行。然而,在某些特定場合只需要單純的影像辨識系統,這個系統可能只需運作幾分鐘完成某些特定功能,即可令其待機或關閉電源,例如:應用於肢體障礙者的自動化嘴控開關遞送裝置,若以電腦 (計算機) 作為該影像辨識系統控制平台,長時間待機只為了在短時間內執行特定影像辨識工作,而其它長時間可能只是空耗電力以等待下一個不知何時會執行的指令,這種做法既不實用也不環保,更浪費能源。因此,在地球資源日益耗竭的今日,開發一套以低功耗為訴求的可攜式特定功能影像辨識系統是一個刻不容緩的議題。

低功耗影像辨識系統的其中一種做法,是以硬體數位電路負責影像處理運算,搭配微處理器執行特定控制程式,取代電腦完成特定用途的即時影像辨識、進而控制硬體裝置,如機械手臂。硬體數位電路在大部分時間處於待機的應用模式下,只須提供少量功率消耗給系統維持感應觸發裝置,當系統一旦接收到啟動信號、立即進入運作模式時,則提供充足電力給整個系統,以進行完整的影像辨識;在取得辨識結果後,完成預定的控制動作,隨即再次進入省電待機模式,既可符合綠能、環保的新設計需求,更可節省不必要成本。

本篇論文主要針對以數位電路構成影像辨識運算核心模組的一種方法做論述,硬體部分主要由可程式化的FPGA (Field-Programmable Gate Array) 組成數位電路系統、並搭配8051微處理器,完成特定影像辨識功能,再將影像辨識完的結果,以命令方式,完成對機械手臂的自動控制動作。在本論文中,也提出所謂的可程式化顏色表 (programmable color tone table),藉由適當地編輯該顏色表,即可使影像辨識系統能夠因應不同環境及光線的條件。當應用於人臉辨識時,不需要為了適應所有不同的膚色,而刻意放寬檢測範圍;採用不同組顏色表,則可對映應用在複雜的多種物體辨識工作。本論文整合影像辨識與機械手臂的功能,成為一套自動化嘴控開關遞送裝置的系統應用。而本論文在驗證系統功能的臨床測試過程中,四名正常健康受測者 (兩名成人、兩名兒童) 測試的成功機率均高於98%,而在脊髓損傷個案的臨床測試成功機率亦可達93%。顯見本系統在功能性以及實用性方面已臻完備,未來更期望能讓肢體障礙的使用者,可以利用本論文提出的影像辨識系統與多種輔具裝置互動,並加入人性化層面的考量,以提升本系統的多功能性與實用性。
An image recognition system usually needs huge resource and is composed of complex parts. These parts include: plenty of memory components, CPU/GPU with rapid operation capability, image capture device or camera, and display devices/interfaces. Recently most of image recognition systems are based on computer system due to modern personal computer (PC) technology that could handle the complex image calculations generally. However, for some special case such as the delivery system of mouth-controlled device (a pacifier switch) for people with disabilities, pure image recognition function is required to operate only for a few minutes during the delivery process, and then the delivery system could be turned off or standby. If the pacifier delivery system is a PC-based image recognition system and it always wait for a long period just to finish only for a few minutes of special task should not meet the requirements of power consumption issue. Therefore, developing a low power portable image recognition system for a special task is a great urgency issue during the time of the depletion of natural resources.

One of the methods to design a low power image recognition system is to integrate the embedded digital logic circuits hardware with the microprocessor, replacing PC to complete the image recognition process and control some special devices such as a robot arm. The embedded image recognition system is in standby mode for most of the time, the low power is needed only for the sensors in the meantime. The system will enter the operating mode to execute the image recognition task while it receives the activation signal from the triggered sensor. Once the procedure of image recognition and control task is finished, the system will enter the standby mode again to save the power consumption.

This thesis reveals a method of image recognition algorithm based on FPGA (Field-Programmable Gate Array), combining with an 8051 microprocessor. A programmable color tone table is defined and adopted in this thesis to make the system suitable for different environments/luminance by editing suitable tone table. It is not necessary to loosen the detection criterion to fit all kinds of skin color for face recognition. The programmable color tone table also can be applied to more complex of multiple object recognition by changing default values in the tables. The portable embedded pacifier delivery system we developed integrated image recognition techniques with robot arm control skills and has been proved that it can function successfully. The accuracy of the system is above 98% when it was tested clinically for 4 normal subjects (two adults and two children). Furthermore the accuracy of test for the subject with severe spinal cord injury is also above 93%. Obviously, the pacifier delivery system is complete in functionality and practicability. We expect that the people with disabilities can interact with more assistive devices with the help of the image recognition system combining with more humanity in the future.
摘要...................................i
Abstract............................iii
誌謝..................................vi
目錄.................................vii
圖目錄................................xi
表目錄................................xv
第一章 序論.............................1
1.1 前言...............................1
1.2 研究動機與目的.......................2
1.3 文獻探討............................3
1.4 論文架構............................6
第二章 演算法模擬.........................7
2.1 影像辨識程序.........................7
2.2 顏色檢測............................8
2.3 雜訊去除...........................10
2.4 邊緣檢測...........................13
2.5 影像物件連結........................15
2.6 演算法模擬結果.......................19
第三章 系統架構及設計.....................20
3.1 系統架構............................20
3.1.1 電路板組成.........................21
3.1.2 可程式化顏色表.....................22
3.2 硬體電路設計及評估.....................23
3.2.1 攝影鏡頭模組長距離傳輸介面............23
3.2.2 VGA介面板.........................24
3.2.3 聲音放大檢測電路....................25
3.2.4 機械手臂電源繼電器開關..............26
3.2.5 PC監測控制介面.....................27
3.2.6 記憶體容量評估.....................28
3.3 FPGA設計...........................29
3.3.1 攝影鏡頭模組影像與控制..............30
3.3.2 SRAM影像資料存取..................30
3.3.2.1 與攝影機及VGA介面的資料存取........31
3.3.2.2 由UART介面讀取資料..............33
3.3.3 VGA影像顯示......................34
3.3.4 UART介面.........................35
3.3.5 顏色表與EEPROM...................36
3.3.6 影像處理單元......................37
3.3.6.1 影像遮罩記憶體與影像buffer的切換...39
3.3.6.2 常態化r-g色彩空間轉換............40
3.3.6.3 顏色檢測.......................41
3.3.6.4 二值化影像侵蝕與擴張.............42
3.3.6.5 二值化影像物件連結...............44
3.3.6.6 物件序號之管理..................47
3.3.6.7 二值化影像物件整理...............49
3.3.6.8 檢視影像處理時序.................51
3.3.7 8051微處理器介面..................52
3.3.7.1 FPGA-微處理器介面信號............52
3.3.7.2 FPGA-微處理器介面指令............53
3.3.8 聲音檢測.........................54
3.3.9 FPGA使用狀況.....................55
3.4 微處理器程式流程....................56
3.4.1 主程式流程.......................56
3.4.2 PC監測控制介面...................58
3.4.3 攝影鏡頭模組參數設定...............60
第四章 系統整合測試.....................61
4.1 顏色表變更測試......................61
4.1.1 特定顏色物體測試..................61
4.1.2 人體膚色測試.....................62
4.2 影像辨識定位效果測試.................63
4.2.1 FPGA即時影像物件資料測試...........63
4.2.2 整合多組影像物件資料的定位測試.......67
4.3 聲音啟動裝置測試....................72
4.4 整合電路系統與機械手臂進行最終測試 .....72
4.4.1 系統整合.........................72
4.4.2 系統測試........................75
4.4.2.1 定位範圍及定位誤差測試...........75
4.4.2.2 臨床測試.......................77
第五章 討論、結論及未來展望...............80
5.1 討論..............................80
5.1.1 PCB的信號完整性問題................80
5.1.2 影像處理相關議題..................82
5.1.3 定位誤差問題分析..................83
5.2 結論..............................85
5.3 未來展望...........................86
參考文獻...............................88
附錄..................................91
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