以電腦視覺結合微型元啟發式優化人工智慧搭載無人機即時檢測橋梁背板劣化維護成本推估系統

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研究生：	劉琦允 Chi-Yun Liu
論文名稱：	以電腦視覺結合微型元啟發式優化人工智慧搭載無人機即時檢測橋梁背板劣化維護成本推估系統 Real-time Underneath Bridge Deck Deterioration Detection and Maintenance Cost Estimation System with Computer Vision and Metaheuristic-Optimized AI Deployed on Unmanned Aerial Vehicle
指導教授：	周瑞生 Jui-Sheng Chou
口試委員:	曾惠斌 Hui-Ping Tserng 王維志 Wei-Chih Wang 鄭明淵 Min-Yuan Cheng 楊亦東 I-Tung Yang 周建成 Chien-Cheng Chou 周瑞生 Jui-Sheng Chou
學位類別：	博士 Doctor
系所名稱：	工程學院 - 營建工程系 Department of Civil and Construction Engineering
論文出版年：	2023
畢業學年度：	112
語文別：	英文
論文頁數：	267
中文關鍵詞：	人工智慧物聯網、邊緣運算、嵌入式系統、電腦視覺、深度學習、微型元啟發式演算法、微型人工智慧、營建智慧管理、關鍵基礎防災檢測、維護成本推估、即時輔助決策、營建管理
外文關鍵詞：	AI Internet of Things (AIoT), edge computing, embedded system, computer vision, deep learning, tiny metaheuristic optimization algorithm, tinyAI, intelligent construction management, disaster prevention and damage inspection for key infrastructures, maintenance cost estimation, decision making, construction management
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人工智慧應用需架設在完善的資訊基礎建設下，倚賴網路連線遠端超級電腦進行資料存取及運算；然而土木營建工程場域多處於逢山開路、遇水架橋的荒山野嶺，環境惡劣且通訊網路普遍架構不甚完善。嵌入式系統於營建場域離線執行邊緣運算人工智能分析，實為土木工程產業重要發展方向之ㄧ。然而，嵌入式系統透過感測器接收的實際資訊仍需經影像數據萃取處理及快速的人工智能分析，方得以衍生即時預測模式與應用。緣此，本研究應用貝氏優化演算法及創新提出一名為「媽祖朝聖行走最佳化(Pilgrimage Walk Optimization, PWO)」的元啟發式優化演算法，進階優化監督式及非監督式人工智慧模型自動化識別橋梁劣化的預測精確性。媽祖朝聖行走最佳化演算法靈感啟發於臺灣獨特的媽祖遶境民間習俗，其拓樸探索行徑模擬虔誠信徒之步履，隨媽祖鑾轎而行，歷經擲筊、遶境、駐駕、鑽轎腳、搶轎及安座等民俗信仰活動。為適用人工智慧系統單晶片(AI SoC)的運算效能，接續改良PWO演算法的拓樸機制，提出媽祖朝聖行走最佳化-輕量版(PWO-Lite)演算法，將其優化微型深度學習模型(tinyAI)，結合邊緣運算開發即時檢測橋梁劣化維護成本推估系統，內嵌於嵌入式系統並搭載於無人飛行載具。研究開發的人工智慧無人飛行載具可輔助傳統人工作業、降低勞工現地巡檢危害風險，並有效提升橋梁劣化檢測品質。同時，擷取的劣化圖像資訊將轉化為工程維修數據，配合維修策略呈現視覺化統計圖表，供該領域專家、工程師作為維護工程成本推估輔助決策依據，並為橋梁管理單位或施工廠商日後橋梁檢修之參考。

The application of artificial intelligence (AI) requires the presence of comprehensive information infrastructures and relies on Internet connection to remote supercomputers for data access and computation. However, civil construction sites are typically located off the beaten track and in locations challenging to access. Such environments are usually unfavorable and have inadequate telecommunication network infrastructures. Therefore, embedded systems capable of performing edge computing and AI analyses offline at construction sites are a major focus of development for the civil engineering industry. Nevertheless, knowledge gap and discrepancy exist between sensor data received by embedded systems and information that civil engineering businesses wish to obtain. Accordingly, the received data must be processed through image data extraction for quick AI analyses, in turn facilitating real-time prediction models and applications. Hence, this research leverages the bayesian optimization algorithm and proposes an innovative metaheuristic optimization algorithm named "Pilgrimage Walk Optimization (PWO)" to enhance the predictive accuracy of both supervised and unsupervised AI models for automated detection of bridge deterioration. The inspiration behind the PWO algorithm is rooted in the distinctive Taiwanese folk tradition of the Matsu pilgrimage, which replicates the footsteps of devoted worshippers accompanying the Matsu palanquin and engaging in various folk religious practices, including divination block casting, pilgrimage, leisure ceremonies, crawling beneath the palanquin, palanquin robbing, and the return palanquin ceremony. To align with the computational efficiency of the AI System-on-Chip (SoC), a lightweight topology mechanism is proposed following improvements to the PWO algorithm. The streamlined version of PWO, named the PWO-Lite algorithm, is employed to fine-tune a tiny deep learning (DL) model. The fine-tuned DL model is subsequently integrated with edge computing for real-time detection of bridge deterioration and maintenance cost estimation. Following this integration, the model is embedded into a system and deployed on unmanned aerial vehicles (UAV). The AI UAV developed in this study can assist in traditional manual operations, mitigating on-site inspection risks for workers while significantly improving the quality of bridge deterioration detection. Simultaneously, the collected deterioration image data will be converted into engineering maintenance data. This data, when combined with maintenance strategies, will be visualized in statistical charts. These charts serve as decision support tools for experts and engineers in the field, facilitating the estimation of maintenance costs. Furthermore, they offer reference information to bridge management agencies and construction companies for future bridge inspections and repairs.

摘要	i
ABSTRACT	iii
ACKNOWLEDGEMENTS	v
TABLE OF CONTENTS	vii
LIST OF FIGURES	xi
LIST OF TABLES	xiii
CHAPTER 1: INTRODUCTION	1
1.1 Research Background and Motivations	1
1.2 Research Objectives	2
1.3 Dissertation Organization	4
CHAPTER 2: LITERATURE REVIEW	7
2.1 Metaheuristic Optimization Algorithms	7
2.2 Deep Learning for Bridge Degradation Detection	10
2.3 Fine-Tuning Hyperparameters in Artificial Intelligence Models	12
2.4 Applying Deep Learning with Object Tracking Algorithms	13
2.5 AI-Embedded Systems and Edge Computing in the Construction Management	15
CHAPTER 3: METHODOLOGY	19
3.1 Supervised Deep Learning Models and Estimation of Distribution Algorithm	19
3.1.1 YOLACT	19
3.1.2 Mask R-CNN	19
3.1.3 CenterMask	20
3.1.4 Estimation of Distribution Algorithm: Bayesian Optimization Algorithm (BOA)	20
3.2 Unsupervised Machine Learning Model: K-means	21
3.3 Supervised Tiny Deep Learning Model and Multi-Object-Tracking Algorithm	22
3.3.1 Tiny Deep Learning Model: YOLOv7-tiny	22
3.3.2 Multi-Object-Tracking Algorithm: DeepSORT	22
3.4 Model Performance Evaluation Metrics	24
CHAPTER 4: PILGRIMAGE WALK OPTIMIZATION (PWO) AND PWO-LITE ALGORITHMS	27
4.1 Introduction to the Matsu Pilgrimage	27
4.2 Developing Metaheuristic Optimization Algorithm: PWO Algorithm	29
4.2.1 Population Initialization: Divination-Block Casting	32
4.2.2 Exploration and Exploitation: Pilgrimage Phase	33
4.2.2.1 Matsu Pilgrimage Movement	33
4.2.2.2 Matsu Random Movement	35
4.2.3 Exploitation: Bobee Phase	35
4.2.3.1 Leisure Ceremony	35
4.2.3.2 Crawling Beneath the Palanquin	37
4.2.3.3 Palanquin Robbing	38
4.2.4 Time-Governing Mechanism	39
4.2.5 Boundary Constraints	39
4.2.6 Optimal Solution Acquisition: Iterative Search and Return Ceremony	40
4.2.7 Validation of the PWO Algorithm	40
4.2.7.1 Fifty Common Benchmark Functions	41
4.2.7.2 Parameter Settings	46
4.2.7.3 Performance and Convergence Comparison	47
4.3 Developing Metaheuristic Optimization Algorithm: PWO-Lite Algorithm	60
4.3.1 Algorithm Lightweight Design Strategy	60
4.3.2 Validation of the PWO-Lite Algorithm	62
4.3.2.1 Six Representative Mathematical Benchmark Functions	62
4.3.2.2 Parameter Settings	62
4.3.2.3 Performance and Convergence Comparison	62
CHAPTER 5: HYBRIDIZATION OF ARTIFICIAL INTELLIGENCE MODELS WITH OPTIMIZATION ALGORITHMS	69
5.1 BOA-Mask R-CNN	69
5.2 PWO-K-means	70
5.3 PWO-Lite-YOLOv7-tiny	71
CHAPTER 6: APPLICATION OF HYBRID ARTIFICIAL INTELLIGENCE MODELS IN BRIDGE INSPECTIONS	73
6.1 Supervised Image Segmentation for Bridge Deterioration Identification	73
6.1.1 Bridge Degradation Statistics	73
6.1.2 Experimental Design for Field Data Collection	76
6.1.2.1 Preliminary Bridge Survey	76
6.1.2.2 Bridge Inspection	76
6.1.3 Creation of Datasets	77
6.1.3.1 Image Collection for Bridge Deterioration	77
6.1.3.2 Pre-Processing of Datasets	78
6.1.4 Construction and Validation of Instance Segmentation Model	80
6.1.4.1 Predictions Using Instance Segmentation	80
6.1.4.2 Results with Augmentation	82
6.1.5 Optimizing Hyperparameters of the Instance Segmentation Model	83
6.1.5.1 Predictions with the Hybrid Instance Segmentation Model	83
6.1.5.2 Relevance and Correlation Analysis of Hyperparameters	88
6.2 Unsupervised Image Segmentation for Bridge Deterioration Identification	90
6.2.1 Image Data Collection for Bridge Deterioration	90
6.2.2 Comparing the Performance of Unsupervised Image Segmentation Models	91
6.3 Supervised Object Detection for Bridge Deterioration Identification	99
6.3.1 Data Collection and Preprocessing of Dataset	99
6.3.2 Predictive Performance of the YOLOv7-tiny Model	103
6.3.3 Predictive Performance of the PWO-Lite-YOLOv7-tiny Model	104
CHAPTER 7: DEVELOPING AN EDGE COMPUTING SYSTEM FOR REAL-TIME DETERIORATION DETECTION AND MAINTENANCE COST ESTIMATION	109
7.1 Real-Time Bridge Deterioration Detection and Quantity Estimation	109
7.2 Establishing an AI-Embedded Cost Maintenance System	114
CHAPTER 8: CONCLUSIONS AND RECOMMENDATIONS	119
8.1 Reviewing and Reaffirming Research Purposes	119
8.2 Research Contributions	119
8.3 Research Limitations and Future Directions	123
REFERENCES	125
APPENDIX A: HARDWARE AND SOFTWARE CONFIGURATION FOR MODEL DEVELOPMENT	137
A.1 Hardware and Software Configuration	137
A.2 Model Development Environment Deployments	137
APPENDIX B: CODING SAMPLES	145
B.1 PWO Algorithm	145
B.2 PWO-Lite Algorithm	165
B.3 Supervised Image Segmentation using BOA-Mask R-CNN	172
B.4 Unsupervised Image Segmentation using PWO-K-means	176
B.5 Supervised Object Detection with PWO-Lite-YOLOv7-tiny	181
B.6 Accelerating Model Inference using TensorRT	197
B.7 AI-Embedded Edge Computing Platform	201
APPENDIX C: TUTORIALS	215
C.1 PWO Tutorial: Benchmark Functions	215
C.2 PWO-Lite Tutorial: Benchmark Functions	216
C.3 BOA-Mask R-CNN Tutorial: Supervised Image Segmentation for Bridge Deterioration Identification	217
C.4 PWO-K-means Tutorial: Unsupervised Image Segmentation for Bridge Deterioration Identification	219
C.5 PWO-Lite-YOLOv7-tiny Tutorial: Supervised Object Detection for Bridge Deterioration Identification	220
C.6 Tutorial: Using TensorRT for Accelerated Deep Learning Inference	221
C.7 AI-Embedded Cost Maintenance System Operation Tutorial	225
APPENDIX D: DETERIORATION DATASETS	229
D.1 Composite Bridge Deterioration Datasets	229
D.2 Concrete Bridge Deterioration Datasets	254
APPENDIX E: REAL-TIME DETECTION RESULTS	257
E.1 Data Flow of Real-Time Deterioration Detection.	257
E.2 Table of Detected Deterioration Ranges and Actual Measurements	266
                                

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全文公開日期 2026/01/12 (校內網路)
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