DETECTION OF RED-COCKADED WOODPECKER HABITATS USING YOLO ALGORITHMS

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dc.contributor.advisorCho, Hyuk
dc.contributor.committeeMemberZhou, Bing
dc.contributor.committeeMemberAn, Min Kyung
dc.contributor.committeeMemberLiu, Qingzhong
dc.creatorde Lemmus, Emerson
dc.date.accessioned2022-08-24T19:16:19Z
dc.date.available2022-08-24T19:16:19Z
dc.date.created2022-08
dc.date.issued2022-08-01T05:00:00.000Z
dc.date.submittedAugust 2022
dc.date.updated2022-08-24T19:16:20Z
dc.description.abstractHabitat and population monitoring are crucial for the preservation of endangered species. However, gathering habitat data may be a hazardous and laborious task. As a result, wildlife ecologists increasingly turn to remote sensing and automation to collect large-scale ecological data on a given species. Particularly, the red-cockaded woodpecker (RCW) is a species endemic to the southeastern United States. Endangered since 1973, wildlife biologists have performed pedestrian surveys to assess the status of the species. Through close interdisciplinary collaboration with ecologists, this work conducts a pilot study that automatically detects potential habitats of RCW. The dataset of 978 images was collected by a team of wildlife ecologists from Raven Environmental Inc. using unmanned aerial vehicles (UAVs). RCW habitat imagery is unique and unavailable in the public domain, thus considered novel image data. The primary goal of this research is to assess the RCW habitat detection performance by You Only Look Once (YOLO) object detection algorithms. Due to the demanding computing requirements of YOLO algorithms, only two small models, YOLOv4-tiny and YOLOv5n, are employed and assessed for this study. The best hyperparameter values are identified for each model to maximize accuracy performance. YOLOv4-tiny reached a training mAP (minimum Average Precision) of 0.96 (i.e., 96%) and a testing accuracy of 0.85 (i.e., 85%), while YOLOv5n achieved a training mAP of 0.78 (i.e., 78%) and a testing accuracy of 0.82 (82%). Overall, combining the inference results of both models achieved a 100% detection of de facto habitats. This study realizes a real-time platform that integrates computer vision with domain knowledge and identifies potential habitats from large-scale image data. Therefore, the deployment of this study on wildlife ecosystems will significantly assist wildlife biologists in saving personnel hours through real-time detection of potential habitats and accelerating proactive field validating for the preservation of RCW.
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/20.500.11875/3635
dc.language.isoEnglish
dc.subjectComputer Science
dc.titleDETECTION OF RED-COCKADED WOODPECKER HABITATS USING YOLO ALGORITHMS
dc.typeThesis
dc.type.materialtext
thesis.degree.collegeCollege of Science and Engineering Technology
thesis.degree.departmentComputer Science
thesis.degree.disciplineComputing and Data Science
thesis.degree.grantorSam Houston State University
thesis.degree.nameMaster of Science

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