Improving Buoy Detection with Deep Transfer Learning for Mussel Farm
Automation
- URL: http://arxiv.org/abs/2308.09238v2
- Date: Mon, 26 Feb 2024 08:54:21 GMT
- Title: Improving Buoy Detection with Deep Transfer Learning for Mussel Farm
Automation
- Authors: Carl McMillan, Junhong Zhao, Bing Xue, Ross Vennell, Mengjie Zhang
- Abstract summary: The aquaculture sector in New Zealand is experiencing rapid expansion, with a particular emphasis on mussel exports.
As the demands of mussel farming operations continue to evolve, the integration of artificial intelligence and computer vision techniques is emerging as an effective approach to enhance operational efficiency.
This study delves into advancing buoy detection by leveraging deep learning methodologies for intelligent mussel farm monitoring and management.
- Score: 7.906113472259946
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The aquaculture sector in New Zealand is experiencing rapid expansion, with a
particular emphasis on mussel exports. As the demands of mussel farming
operations continue to evolve, the integration of artificial intelligence and
computer vision techniques, such as intelligent object detection, is emerging
as an effective approach to enhance operational efficiency. This study delves
into advancing buoy detection by leveraging deep learning methodologies for
intelligent mussel farm monitoring and management. The primary objective
centers on improving accuracy and robustness in detecting buoys across a
spectrum of real-world scenarios. A diverse dataset sourced from mussel farms
is captured and labeled for training, encompassing imagery taken from cameras
mounted on both floating platforms and traversing vessels, capturing various
lighting and weather conditions. To establish an effective deep learning model
for buoy detection with a limited number of labeled data, we employ transfer
learning techniques. This involves adapting a pre-trained object detection
model to create a specialized deep learning buoy detection model. We explore
different pre-trained models, including YOLO and its variants, alongside data
diversity to investigate their effects on model performance. Our investigation
demonstrates a significant enhancement in buoy detection performance through
deep learning, accompanied by improved generalization across diverse weather
conditions, highlighting the practical effectiveness of our approach.
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