Seal Counting on our Plages (S.C.O.O.P.)

The Vertebrate Integrative Physiology (VIP) lab monitors the population of northern elephant seals at the largest mainland breeding colony, located at Piedras Blancas (San Simeon, CA). As the population expands, more human-seal interactions and conflicts over land use occur. The VIP lab's work informs California State Parks and helps with the management of the rookery. Currently, members of the VIP lab fly a drone over the beaches, capture multiple images, and manually count the seals, which takes around 14 to 21 hours of analysis per survey. Machine learning methods such as Convolutional Neural Networks (CNN) and Region-based Convolutional Neural Networks (RCNN) have been shown to quickly and accurately determine the count but require lots of data, which is not feasible for this task due to the 79 available beach images. By dividing larger beach images into smaller sub-images, it is possible to generate more data, facilitating the use of deep learning techniques. This thesis outlines a pipeline to use these sub-images and determine the seal count of a beach image.

Publication Details

Journal
DigitalCommons - CalPoly (California State Polytechnic University)
Published
2024-09-01
Primary Topic
Marine animal studies overview
Type
article
Field-Weighted Citation Impact
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article

Seal Counting on our Plages (S.C.O.O.P.)

DigitalCommons - CalPoly (California State Polytechnic University)
Marine animal studies overview
article

Seal Counting on our Plages (S.C.O.O.P.)

article en

Abstract

The Vertebrate Integrative Physiology (VIP) lab monitors the population of northern elephant seals at the largest mainland breeding colony, located at Piedras Blancas (San Simeon, CA). As the population expands, more human-seal interactions and conflicts over land use occur. The VIP lab's work informs California State Parks and helps with the management of the rookery. Currently, members of the VIP lab fly a drone over the beaches, capture multiple images, and manually count the seals, which takes around 14 to 21 hours of analysis per survey. Machine learning methods such as Convolutional Neural Networks (CNN) and Region-based Convolutional Neural Networks (RCNN) have been shown to quickly and accurately determine the count but require lots of data, which is not feasible for this task due to the 79 available beach images. By dividing larger beach images into smaller sub-images, it is possible to generate more data, facilitating the use of deep learning techniques. This thesis outlines a pipeline to use these sub-images and determine the seal count of a beach image.

DigitalCommons - CalPoly (California State Polytechnic University)
Openalex Percentile: Top 41%
Marine animal studies overview
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