Just as the governor announced the start of the Florida python hunting season this month, researchers at the University of Central Florida released a one-of-a-kind study that shows that near-infrared (NIR) cameras ) can help hunters more effectively. hunt down these invasive snakes, especially at night.
Snakes, which can grow to 26 feet long and 200 pounds, have invaded the Florida Everglades, threatening native species and disrupting the ecosystem. The number of common native species seen in the Everglades since snakes were first discovered in the 1990s fell 90% in some species until 2010, according to an earlier study. Since then, the state has implemented mitigation strategies and encouraged residents to hunt down the huge snakes. On average, snakes removed from the Everglades are about eight feet long, according to the Florida Fish and Wildlife Conservation Commission.
The new study found that using NIR cameras, pythons could be detected 20% further away than with visible cameras. The researchers say that with more work, they might be able to develop an automated snake detection system. This could be a game-changer, especially as pythons walk north and could threaten native species as far north as Virginia and Texas to the west.
“Manual removal of Burmese pythons has been the most effective management strategy, but snakes are difficult to see due to their natural camouflage,” says Kyle Renshaw, study co-author and assistant professor at CREOL – UCF Optics College and Photonics. “NIR cameras will help python hunters find and remove pythons. These inexpensive little cameras could be mounted on trucks or drones to help catch hard-to-find pythons. The use of cameras also opens up the possibility. automated detection using computer algorithms to search for images faster and more comprehensively than hunters can do on their own. ”
Jennifer Hewitt, a graduate student from Renshaw’s lab led the study, which was published this week in the journal Applied optics.
This research is an example showing how cameras can be “tuned” to improve the performance of a specific task, according to the researcher. Detection band, time constants, lens settings, image processing, and algorithms provide a rich set of variables to optimize a camera system for a particular application. Work with the snakes was based on observation from a stationary position, but the team hopes to expand their work to include mobile sensors.
“It could have great applications in search and rescue, explosives detection, border security, etc.,” Renshaw said. “Jen is developing and testing models for time-limited research using a mobile camera as we speak as part of her dissertation.”
How did they do it?
The work is based on a previous characterization of the spectral reflectivity of pythons carried out at CREOL by Professor UCF Ron Driggers.
Hewitt collected images of pythons from different locations and with different background settings. The images were collected using two similar cameras which differed only in their spectral sensitivity. The images were taken day and night at 10 different locations with similar foliage.
Hewitt then wrote software to randomly present images to the volunteers, asking them to “click” on the snake in the scene. Some scenes did not have snakes. User responses were collected and analyzed. The volunteers spent an hour looking at images on a computer to locate pythons and clicking on the python image.
“For daytime and nighttime conditions, the volunteers were able to detect the pythons farther away with the NIR than with the visible,” Hewitt explains. “From there, we continue to refine the camera system to further improve the detection rate. “
NIR cameras have been tuned to snakes and appear to be more effective at night as the snake’s camouflage does not create the same glare as in the sun.
Renshaw joined UCF in 2015. He holds a doctorate in applied physics and a master’s degree in electrical engineering from the University of Michigan. He also holds a bachelor’s degree in engineering physics from Cornell University. He heads the Thin Film Optoelectronics Laboratory (TFO), which conducts research and development activities on materials, components and technologies for imaging systems.
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