A team of students at Cal Poly is working to combine drones and artificial intelligence to ‘forecast’ shark encounters and to get beachgoers safely into the water again.
Cal Poly students use artificial intelligence (AI), drones to ‘forecast’ shark encounters
The students have teamed up with Cal State Long Beach Shark Lab to train the computer learning system to recognize sharks in real-time. A system to forecast shark encounters for surfers and swimmers is also in the works.
With the use of enhanced artificial intelligence methods and drone video footage, the Cal Poly research project aims to identify sharks and other objects in the water and two alert lifeguards in real-time to keep both people and sharks safe.
The Cal State Long Beach Shark Lab is well known for developing innovative technologies that include drones and underwater cameras to learn more about sharks in their natural environment.
According to Chris Lowe, Director of the Shark Lab, many people are hesitant to get into the water since the white shark populations along the central and southern California coastline have increased.
“The goal is to automate the ingestion of drone video data and flight metadata to generate spatially explicit information on water recreational activities — when, where, and who is interacting with sharks along the coast,” said Lowe, according to KEYT.
The research project is part of this year’s Summer Undergraduate Research Program, better known as the SURP program in Cal Poly’s College of Engineering. The program teams up undergraduate students with faculty mentors and industry to conduct meaningful, real-world research.
The Cal Poly students assisted the Shark Lab to develop machine learning algorithms, also known as neural networks, to identify water users such as surfers, swimmers, and bodyboarders based on aerial footage from drones.
“This is no easy task because light, sea conditions, and even the position of the water user on a board will matter,” Lowe said.
Cal Poly’s inter-disciplinary team of students has a variety of backgrounds in computer science and related fields, as well as an interest in marine biology.
The students used more than 1,300 images of sharks, seals, boats, surfers, and swimmers to help and improve the machine learning model.
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