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A geometric model with stochastic error for abnormal motion detection of portal crane bucket grab

题目A geometric model with stochastic error for abnormal motion detection of portal crane bucket grab
作者Yu, Baichen Wang, Xiao Wang, Hansheng
作者单位Peking Univ, Guanghua Sch Management, 5 Yiheyuan Rd, Beijing 100871, Peoples R China Qingdao Univ, Sch Math & Stat, 308 Ningxia Rd, Qingdao 266071, Peoples R China
关键词：GENETIC ALGORITHM SYSTEM SELECTION SENSOR
时间：2025年1月1日
出版者：ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
摘要Abnormal swing angle detection of bucket grabs is crucial for efficient harbor operations. In this study, we develop a practically convenient swing angle detection method for crane operation, requiring only a single standard surveillance camera at the fly-jib head, without the need for sophisticated sensors or markers on the payload. Specifically, our algorithm takes the video images from the camera as input. Next, a fine-tuned 'the fifth version of the You Only Look Once algorithm' (YOLOv5) model is used to automatically detect the position of the bucket grab on the image plane. Subsequently, a novel geometric model is constructed, which takes the pixel position of the bucket grab, the steel rope length provided by the Programmable Logic Controller (PLC) system, and the optical lens information of the camera into consideration. The key parameters of this geometric model are statistically estimated by a novel iterative algorithm. Once the key parameters are estimated, the algorithm can automatically detect swing angles from video streams. Being analytically simple, the computation of our algorithm is fast, as it takes about 0.01 s to process one single image generated by the surveillance camera. Therefore, we are able to obtain an accurate and fast estimation of the swing angle of an operating crane in real-time applications. Simulation studies are conducted to validate the model and algorithm. Real video examples from Qingdao Seaport under various weather conditions are analyzed to demonstrate its practical performance.
URLhttp://hdl.handle.net/20.500.11897/725997
ISSN0952-1976
DOI10.1016/j.engappai.2024.109481
收录情况SCI€
作者单位 Peking Univ, Guanghua Sch Management, 5 Yiheyuan Rd, Beijing 100871, Peoples R China Qingdao Univ, Sch Math & Stat, 308 Ningxia Rd, Qingdao 266071, Peoples R China 时间 2025年1月1日
出版者 ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE URL http://hdl.handle.net/20.500.11897/725997
ISSN 0952-1976 DOI 10.1016/j.engappai.2024.109481
收录情况 SCI€ 分类
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