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標題: | 應用深度學習於海面下金屬侵蝕辨識–以大鵬灣海域為例 Application of Deep Learning Method to Underwater Rust Image Recognition–In Case of Dapeng Bay |
作者: | 盧湘凱 Hsiang-Kai Lu |
指導教授: | 陳柏翰 Po-Han Chen |
關鍵字: | 港灣工程,深度學習,Mask R-CNN,海面下鋼材侵蝕之辨識,半自動檢測方式, harbor engineering,deep learning,Mask R-CNN,identification of steel erosion under the sea surface,semi-automatic detection method, |
出版年 : | 2023 |
學位: | 碩士 |
摘要: | 在港灣工程專案裡,金屬鋼材廣泛運用於結構支撐關鍵材料,然海洋生物附著與海水成分容易造成腐蝕,長時間浸泡於海水對鋼材之支撐力造成損害。舉例來說,鋼板於海中施放三個月後,在穿孔或裁切區域易產生間隙腐蝕現象,使工程進行中裸露於海水的鋼材,在長期使用上存在結構安全的疑慮,必須定時撤換新的鋼材增加施工或營運成本。因此,本研究將以大鵬灣港灣工程為例,進行海水中鋼材耐腐蝕與海洋生物附著之研究。深度學習已經被廣泛應用在各個領域中的深度學習方法,聚焦於海面下鋼材侵蝕之辨識。該學習方法已運用於陸地上鋼材侵蝕之辨識與海面下魚群之辨識,但尚未有研究著重在辨識海面下金屬侵蝕情況,原因為海底下侵蝕照片資料取得不易,再加上海水中生物與光線不足,會使得影像變得模糊不清,增加了辨識難度。由於資料收集不易,故設計一實驗來探討海面下金屬侵蝕發生原因與提供深度學習方法訓練之照片,本實驗使用不鏽鋼試片規格為:長度15公分、寬度7公分、厚度0.3公分、重量平均165公克。試片施放地點為大鵬灣國家風景管理處,因潮汐現象明顯,適合做於本實驗試放地點,本實驗並將試片分成三組:潮間帶組、水下清潔組、水下不動組,水下清潔組指每次實驗紀錄後會刮除試片附著物,反之水下不動組指任其恣意生長,目的為確認清潔是否會造成影響,潮間帶組也會做清潔動作。在深度學習方法中,本研究使用實驗試片水下之侵蝕照片,共蒐集了約300張侵蝕照片作為訓練資料、44張照片作為測試資料訓練,選用Mask R-CNN演算法來進行侵蝕影像辨識,Mask R-CNN為近期非常受歡迎的演算法,它不僅可以快速的偵測出辨識目標,還能準確的將辨識目標的形狀框架判別出來。因此,將上述實驗所獲得的海面下金屬侵蝕照片,用以訓練Mask R-CNN深度學習模型。本研究建立一種半自動檢測方式,隨著未來深度學習技術提升,更有機會將辨識技術用於調查海下結構物安全之用。結果表明,該模型可成功檢測不同表面處理之金屬侵蝕區域。 In the harbor project, metal steel is widely used in the key materials of structural support, but the composition of Marine life is easy to cause corrosion, and soaking in seawater for a long time will cause damage to the support force of steel. For example, after the steel plate is applied in the sea for three months, the gap corrosion phenomenon may occur in the perforated or cutting area, which makes the steel exposed to the seawater have doubts about structural safety in the long-term use, and the new steel must be replaced regularly to increase the construction or operating costs. Therefore, this study will take the Dapeng Bay Marine Project as an example to study the corrosion resistance of steel and Marine life in seawater. This study uses deep learning methods, which have been widely used in various fields, focusing on the identification of steel erosion under the sea surface. The learning method has been applied to the identification of steel erosion on land and fish under the sea surface, but no research has been focused on the identification of metal erosion under the sea surface. The reason is that the photo erosion under the sea surface is not easy to obtain, coupled with the lack of biology and light in the sea, will make the image blurred and increase the difficulty of identification.In this study, an experiment was designed to investigate the causes of metal erosion under the sea surface and provide the training of deep learning methods. The stainless steel test pieces were 15 cm long, 7 cm wide, 0.3 cm thick, and the average weight of 165 grams. Trial casting place for dapeng bay national landscape management, because of the tidal phenomenon is obvious, suitable for this experiment test site, this experiment and will be divided into three groups: intertidal group, underwater cleaning group, underwater group, underwater cleaning group refers to scraping after each experiment record attached objects, while the underwater group arbitrary growth, purpose to confirm whether clean will affect, intertidal group will also do clean action.In deep learning method, this study using the experimental test underwater erosion photos, collected about 300 pieces of erosion photos as training data, 44 photos as test data training, choose Mask R-CNN algorithm to erosion image identification, Mask R-CNN recently very popular algorithm, it can not only quickly detect the target, also can accurately identify the shape of the target framework. Therefore, this study used the sub-surface metal erosion photos obtained from the above experiments to train the Mask R-CNN deep learning model. In this study, a semi-automatic detection method is established. With the improvement of deep learning technology in the future, there is more opportunity to use the identification technology to investigate the safety of offshore structures. The results show that the model can successfully detect the different surface treatments. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88482 |
DOI: | 10.6342/NTU202302452 |
全文授權: | 未授權 |
顯示於系所單位: | 土木工程學系 |
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