Um novo projeto, liderado em conjunto pela Universidade de Stirling e pela start-up holandesa Hack the Planet, desenvolveu pela primeira vez uma câmera de vida selvagem movida a inteligência artificial (IA) que pode beneficiar enormemente...
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)
Tim van Deursen do Hack The Planet instalando uma armadilha fotográfica habilitada para IA no Parque Nacional Lopé Gabão. Crédito: Floris Tils
Um novo projeto, liderado em conjunto pela Universidade de Stirling e pela start-up holandesa Hack the Planet, desenvolveu pela primeira vez uma câmera de vida selvagem movida a inteligência artificial (IA) que pode beneficiar enormemente a conservação ao identificar conflitos humanos-animais em tempo real.
A tecnologia, que pode detectar diferentes espécies de animais e humanos em tempo real, tem o potencial de descobrir conflitos entre pessoas e animais selvagens, bem como atividades ilegais em áreas protegidas, e fornecer alertas ao vivo para ecoguardas.
As câmeras de trilha são usadas regularmente em pesquisas de vida selvagem para detectar ameaças ao ecossistema, mas geralmente são prejudicadas em regiões remotas devido à falta de conectividade de banda larga. O uso de câmeras de vida selvagem com inteligência artificial pode corrigir esse problema, fornecendo alertas instantâneos sem a necessidade de Wi-Fi, rádio de longo alcance ou cobertura de celular, ajudando a conservar, proteger e restaurar melhor os ecossistemas como resultado.
O estudo, intitulado "Alertas em tempo real de armadilhas fotográficas com IA usando a rede de satélites Iridium: um estudo de caso no Gabão, África Central", e publicado na Methods in Ecology and Evolution , identificou com precisão elefantes e humanos em áreas remotas da Gabão, onde a tecnologia foi implantada.
O piloto de pesquisadores e engenheiros da Universidade de Stirling e Hack the Planet, parte do estúdio de produtos digitais Q42, mostra que essas câmeras inteligentes podem ajudar a detectar caçadores furtivos e prevenir conflitos entre humanos e elefantes que ocorrem frequentemente na floresta tropical africana, entre outros locais.
A armadilha de câmera inteligente que eles desenvolveram pode rotular imagens imediatamente graças à IA e, se necessário, enviar um aviso para, por exemplo, guardas florestais ou uma aldeia.
A pesquisa mostra que o monitoramento remoto e as análises off-line podem ser feitas de forma confiável. O sistema também é capaz de evitar conflitos entre humanos e animais, impedindo que os elefantes entrem em uma aldeia em busca de comida.
É a primeira vez que um sistema de câmera tão inovador é rigorosamente testado sob as duras condições de uma floresta tropical. Ao combinar um modelo de IA, armadilhas fotográficas prontas e hardware personalizado com conexão via satélite, agora é possível enviar informações em tempo real para guardas florestais de locais remotos. A pesquisa mostra que análises confiáveis ??podem ser feitas com vistas à conservação da natureza e à ecologia.
Dr. Robin Whytock, pesquisador de pós-doutorado na Universidade de Stirling durante o estudo, disse: "Dados em tempo real de câmeras inteligentes e outros sensores podem revolucionar a forma como monitoramos e protegemos os ecossistemas mais ameaçados do mundo. Os avanços feitos neste estudo mostram que os dados em tempo real podem ser usados ??para tomar melhores decisões durante situações de tempo crítico."
Tim van Deursen, fundador do Hack The Planet, disse: "Com este piloto, demonstramos que nossa tecnologia de câmera com IA funciona e pode ter um impacto positivo na conservação da natureza. Nossa solução não depende da instalação de infraestrutura de rede adicional em a paisagem e pode ser implantado em campo por não especialistas em qualquer lugar do mundo."
Lee White, ministro gabonês da Água, Florestas, Mar e Meio Ambiente, disse: "Menos de nossos eco-guardas morrerão e mais caçadores furtivos serão capturados, se pudermos implantar essa tecnologia".
Durante o piloto na floresta tropical do Gabão, cinco sistemas de câmeras tiraram mais de 800 fotos em 72 dias. Foram tiradas 217 fotos de elefantes. O modelo AI alcançou uma precisão de 82% no reconhecimento de elefantes. Os Rangers receberam um alerta do sistema em sete minutos, em média.
Mais informações: Robin C. Whytock et al, Alertas em tempo real de armadilhas fotográficas com IA usando a rede de satélites Iridium: um estudo de caso no Gabão, África Central, Métodos em Ecologia e Evolução (2023). DOI: 10.1111/2041-210X.14036
Informações da revista: Métodos em Ecologia e Evolução