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http://dx.doi.org/10.7837/kosomes.2020.26.6.601

A Study on the Detection of Fallen Workers in Shipyard Using Deep Learning  

Park, Kyung-Min (Division of Coast guard, Mokpo National Maritime University)
Kim, Seon-Deok (Division of Coast guard, Mokpo National Maritime University)
Bae, Cherl-O (Division of Coast guard, Mokpo National Maritime University)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.26, no.6, 2020 , pp. 601-605 More about this Journal
Abstract
In large ships with complex structures, it is difficult to locate workers. In particular, it is not easy to detect when a worker falls down, making it difficult to respond quickly. Thus, research is being conducted to detect fallen workers using a camera or by attaching a device to the body. Existing image-based fall detection systems have been designed to detect a person's body parts; hence, it is difficult to detect them in various ships and postures. In this study, the entire fall area was extracted and deep learning was used to detect the fallen shipworker based on the image. The data necessary for learning were obtained by recording falling states at the shipyard. The amount of learning data was augmented by flipping, resizing, and rotating the image. Performance evaluation was conducted with precision, reproducibility, accuracy, and a low error rate. The larger the amount of data, the better the precision. In the future, reinforcing various data is expected to improve the effectiveness of camera-based fall detection models, and thus improve safety.
Keywords
Ship; Fallen; Deep learning; Image; Safety;
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Times Cited By KSCI : 4  (Citation Analysis)
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