Computer vision‐assisted photogrammetry and one‐image 3D modeling in marine mammals

Image processing using traditional photogrammetric methods is a labor‐intensive process. The collection of photogrammetry images during aerial surveys is expanding rapidly, creating new challenges to analyze images promptly and efficiently, while reducing human error during processing. Computer visi...

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Bibliographic Details
Published in:Marine mammal science 2024-04, Vol.40 (2), p.n/a
Main Authors: Zhang, Changqun, Zhou, Haojie, Shah, Sheel, Davis, Randall W, Hao, Yujiang, Yung, Kaung‐Ti, Wang, Kexiong, Wang, Ding
Format: Article
Language:English
Subjects:
3D model
Aerial photography
Aerial surveying
Aerial surveys
Artificial intelligence
automation
Body volume (biology)
Computer vision
Computers
contour detection
Error reduction
Freshwater mammals
Human error
Image enhancement
Image processing
Labour
Machine learning
marine mammal
Marine mammals
Modelling
Morphometry
OpenCV
Photogrammetry
Python
Three dimensional models
three‐dimensional
Virtual reality
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Summary:Image processing using traditional photogrammetric methods is a labor‐intensive process. The collection of photogrammetry images during aerial surveys is expanding rapidly, creating new challenges to analyze images promptly and efficiently, while reducing human error during processing. Computer vision‐assisted photogrammetry, a field of artificial intelligence (AI), can automate image processing, greatly enhancing the efficiency of photogrammetry. Here, we present a practical and efficient program capable of automatically extracting the fine‐scale photogrammetry of East Asian finless porpoises (Neophocaena asiaeorientalis sunameri). Our results indicated that computer vision‐assisted photogrammetry could achieve the same accuracy as traditional photogrammetry, and the results of the comparisons were validated against the direct measurements. Three‐dimensional (3D) models using computer vision‐assisted photogrammetric morphometrics generated trustworthy body volume estimates. We also explored the one image‐based 3D modeling technique, which is less accurate, but still useful when only one image of the animal is available. Although several limitations exist in the current program, improvements could be made to narrow the virtual‐reality gap when more images are available for machine learning and training. We recommend this program for analyzing images of marine mammals possessing a similar morphological contour.
ISSN:0824-0469
1748-7692
DOI:10.1111/mms.13083