Journal of the Korean Society of Environmental Restoration Technology (한국환경복원기술학회지)

The Korea Society of Environmental Restoration Technology (한국환경복원기술학회)
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Development of Urban Wildlife Detection and Analysis Methodology Based on Camera Trapping Technique and YOLO-X Algorithm

카메라 트래핑 기법과 YOLO-X 알고리즘 기반의 도시 야생동물 탐지 및 분석방법론 개발

  • Kim, Kyeong-Tae (Dept of Environmental Horticulture and Landscape Architecture, Dankook University) ;
  • Lee, Hyun-Jung (Dept of Environmental Horticulture and Landscape Architecture, Dankook University) ;
  • Jeon, Seung-Wook (School of Data Science, Division of Computer Science and Engineering, Sunmoon University) ;
  • Song, Won-Kyong (Dept of Landscape Architecture, Dankook University) ;
  • Kim, Whee-Moon (Dept of Environmental Horticulture and Landscape Architecture, Dankook University)
  • 김경태 (단국대학교 환경원예.조경학과) ;
  • 이현정 (단국대학교 환경원예.조경학과) ;
  • 전승욱 (선문대학교 소프트웨어 융합대학 컴퓨터공학부 데이터공학과) ;
  • 송원경 (단국대학교 환경원예.조경학부) ;
  • 김휘문 (단국대학교 환경원예.조경학과)
  • Received : 2023.07.28
  • Accepted : 2023.08.28
  • Published : 2023.08.30
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Abstract

Camera trapping has been used as a non-invasive survey method that minimizes anthropogenic disturbance to ecosystems. Nevertheless, it is labor-intensive and time-consuming, requiring researchers to quantify species and populations. In this study, we aimed to improve the preprocessing of camera trapping data by utilizing an object detection algorithm. Wildlife monitoring using unmanned sensor cameras was conducted in a forested urban forest and a green space on a university campus in Cheonan City, Chungcheongnam-do, Korea. The collected camera trapping data were classified by a researcher to identify the occurrence of species. The data was then used to test the performance of the YOLO-X object detection algorithm for wildlife detection. The camera trapping resulted in 10,500 images of the urban forest and 51,974 images of green spaces on campus. Out of the total 62,474 images, 52,993 images (84.82%) were found to be false positives, while 9,481 images (15.18%) were found to contain wildlife. As a result of wildlife monitoring, 19 species of birds, 5 species of mammals, and 1 species of reptile were observed within the study area. In addition, there were statistically significant differences in the frequency of occurrence of the following species according to the type of urban greenery: Parus varius(t = -3.035, p < 0.01), Parus major(t = 2.112, p < 0.05), Passer montanus(t = 2.112, p < 0.05), Paradoxornis webbianus(t = 2.112, p < 0.05), Turdus hortulorum(t = -4.026, p < 0.001), and Sitta europaea(t = -2.189, p < 0.05). The detection performance of the YOLO-X model for wildlife occurrence was analyzed, and it successfully classified 94.2% of the camera trapping data. In particular, the number of true positive predictions was 7,809 images and the number of false negative predictions was 51,044 images. In this study, the object detection algorithm YOLO-X model was used to detect the presence of wildlife in the camera trapping data. In this study, the YOLO-X model was used with a filter activated to detect 10 specific animal taxa out of the 80 classes trained on the COCO dataset, without any additional training. In future studies, it is necessary to create and apply training data for key occurrence species to make the model suitable for wildlife monitoring.

Keywords

Acknowledgement

본 논문은 환경부 한국환경산업기술원의 생태계 기반 탄소흡수원 조성·관리 기술개발사업(RS-2023-00218245)의 지원을 받아 작성되었습니다.

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