Deep Learning for Crack-Like Object Detection

Zhang, Kaige, Cheng, Heng-Da

Deep Learning for Crack-Like Object Detection
  • 出版商: CRC
  • 出版日期: 2023-03-20
  • 售價: $2,350
  • 貴賓價: 9.5 折 $2,233
  • 語言: 英文
  • 頁數: 100
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1032181184
  • ISBN-13: 9781032181189
  • 相關分類: DeepLearning

    • 下單後立即進貨 (約2~4週)

相關主題

商品描述

Computer vision-based crack-like object detection has many useful applications, such as inspecting/monitoring pavement surface, underground pipeline, bridge cracks, railway tracks etc. However, in most contexts, cracks appear as thin, irregular long-narrow objects, and often are buried in complex, textured background with high diversity which make the crack detection very challenging. During the past a few years, deep learning technique has achieved great success and has been utilized for solving a variety of object detection problems.

This book discusses crack-like object detection problem comprehensively. It starts by discussing traditional image processing approaches for solving this problem, and then introduces deep learning-based methods. It provides a detailed review of object detection problems and focuses on the most challenging problem, crack-like object detection, to dig deep into the deep learning method. It includes examples of real-world problems, which are easy to understand and could be a good tutorial for introducing computer vision and machine learning.

商品描述(中文翻譯)

基於電腦視覺的類裂縫物體檢測具有許多有用的應用，例如檢查/監控路面、地下管道、橋樑裂縫、鐵軌等。然而，在大多數情況下，裂縫呈現為細長、不規則的物體，並且通常埋藏在具有高度多樣性的複雜紋理背景中，這使得裂縫檢測非常具有挑戰性。在過去幾年中，深度學習技術取得了巨大的成功，並被用於解決各種物體檢測問題。

本書全面討論了類裂縫物體檢測問題。它首先討論了傳統的圖像處理方法來解決這個問題，然後介紹了基於深度學習的方法。它詳細回顧了物體檢測問題，並專注於最具挑戰性的問題，即類裂縫物體檢測，以深入探討深度學習方法。它包含了易於理解的實際問題示例，可作為介紹計算機視覺和機器學習的良好教程。

作者簡介

Kaige Zhang has a B.S. degree (2011) in electronic engineering from the Harbin Institute of Technology, China, and a Ph.D. degree (2019) in computer science from Utah State University, USA. His research interests include computer vision, machine learning, and the applications on intelligent transportation systems, precision agriculture, and biomedical data analytics. Dr. Zhang has been the reviewer for many top journals in his research areas, such as IEEE Transactions on ITS, IEEE Trans. On T-IV, J. of Comput. in Civil Eng., Scientific Report, etc.

Heng-Da Cheng has a Ph.D. in Electrical Engineering from Purdue University, West Lafayette, IN, USA in 1985 under the supervision Prof. K. S. Fu. He is a Full Professor with the Department of Computer Science, Utah State University, Logan, UT. He has authored over 350 technical papers and is the Associate Editor of Pattern Recognition, Information Sciences, and New Mathematics and Natural Computation.

作者簡介(中文翻譯)

Kaige Zhang在2011年獲得中國哈爾濱工業大學的電子工程學士學位，並在2019年獲得美國猶他州立大學的計算機科學博士學位。他的研究興趣包括計算機視覺、機器學習以及在智能交通系統、精準農業和生物醫學數據分析方面的應用。張博士曾擔任許多頂級期刊的審稿人，如IEEE Transactions on ITS、IEEE Trans. On T-IV、J. of Comput. in Civil Eng.、Scientific Report等。

Heng-Da Cheng於1985年在美國印第安納州西拉法葉的普渡大學獲得電氣工程博士學位，指導教授為K. S. Fu教授。他是猶他州立大學計算機科學系的正教授，已發表超過350篇技術論文，並擔任Pattern Recognition、Information Sciences和New Mathematics and Natural Computation的副編輯。

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