Please use this identifier to cite or link to this item: http://hdl.handle.net/10790/5298

APPLYING OBJECT DETECTION TO MONITORING MARINE DEBRIS

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Title:APPLYING OBJECT DETECTION TO MONITORING MARINE DEBRIS
Authors:Sherwood, Leah
Contributors:Peterson, Michael (advisor)
Tropical Conservation Biology & Environmental Science (department)
Keywords:Environmental science
Computer science
beach surveys
Darknet/YOLO
deep learning
show 3 moremarine debris
object detection
Plastic pollution
show less
Date Issued:May 2020
Publisher:University of Hawaii at Hilo
Abstract:Most of what is known about the type and distribution of plastic marine debris has been learned from beach surveys conducted by hundreds of researchers and volunteers since the 1980s. However, beach surveys of plastic marine debris require significant manual labor and lack harmonization across survey sites. In this thesis, I demonstrate how object detection technology based on deep learning can be deployed to partially automate the manual labor required to conduct beach surveys and upload the survey results to a centralized marine debris database. To create a proof-of-concept implementation, I developed an object detection system for marine debris using Darknet, an open source framework for convolutional neural networks, and the detection algorithm YOLOv3. I trained the detector on nine object classes: bags, bottlecaps, bottles, buoys, containers, hagfish traps, nets, oyster spacers, and “other,” and achieved a mean average precision (the standard metric of accuracy in the object detection literature) of 52%. The best performing class was hagfish trap, with an average precision of 80%, and the worst performing class was “other,” with an average precision of 34%. Next, a team of UH Hilo undergraduates and I migrated the system to the Android smartphone platform using Tiny YOLO, a smaller version of YOLO that was developed for running on low-powered computing devices. I compared the performance of YOLOv3 and Tiny YOLO at different image sizes with and without transfer learning (pre-training). My results demonstrate that it is possible to deploy object detection technology at beach survey sites to identify and count marine debris objects in real time. The technology is also applicable to other scenarios such as monitoring for plastic marine debris underwater or on the ocean surface. Ultimately, I expect the technology to be deployed as part of a “human in the loop” system in which the object detection component interacts with the person performing the beach survey so that the system can continuously improve in accuracy as it is used in the field while reducing the time and human labor costs associated with beach debris surveys.
Pages/Duration:67 pages
URI:http://hdl.handle.net/10790/5298
Rights:All UHH dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
Appears in Collections: Tropical Conservation Biology and Environmental Science
TCBES Theses


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