LANTERN-RD：启用深度学习以减轻入侵的斑点灯笼蝇
LANTERN-RD: Enabling Deep Learning for Mitigation of the Invasive Spotted Lanternfly
摘要：
斑点灯笼蝇（SLF）是一种入侵性稻飞虱，威胁着美国东北部和日本等地区的当地生物多样性和农业经济。随着研究人员争相研究这种昆虫，计算机视觉任务（如检测、姿态估计和准确识别）在遏制SLF方面具有重要的下游影响。然而，目前还没有公开的数据集来训练这种AI模型。为了使计算机视觉应用和激励进步，以挑战侵入性SLF问题，我们提出了LANTERN-RD，第一个策划的图像数据集的斑点灯笼苍蝇及其外观相似，具有不同的照明条件，不同的背景，并在各种姿势的主题的图像。基于VGG 16的基线CNN验证了该数据集在刺激新的计算机视觉应用以加速侵入性SLF研究方面的潜力。此外，我们在一个简单的移动分类应用程序中实现了训练模型，以便直接授权负责任的公共减灾工作。这项工作的首要任务是引入一个新的SLF图像数据集，并发布一个分类框架，使计算机视觉应用成为可能，促进围绕侵入性SLF的研究，并帮助最大限度地减少其农业和经济损失。
英文摘要：
The Spotted Lanternfly (SLF) is an invasive planthopper that threatens the local biodiversity and agricultural economy of regions such as the Northeastern United States and Japan. As researchers scramble to study the insect, there is a great potential for computer vision tasks such as detection, pose estimation, and accurate identification to have important downstream implications in containing the SLF. However, there is currently no publicly available dataset for training such AI models. To enable computer vision applications and motivate advancements to challenge the invasive SLF problem, we propose LANTERN-RD, the first curated image dataset of the spotted lanternfly and its look-alikes, featuring images with varied lighting conditions, diverse backgrounds, and subjects in assorted poses. A VGG16-based baseline CNN validates the potential of this dataset for stimulating fresh computer vision applications to accelerate invasive SLF research. Additionally, we implement the trained model in a simple mobile classification application in order to directly empower responsible public mitigation efforts. The overarching mission of this work is to introduce a novel SLF image dataset and release a classification framework that enables computer vision applications, boosting studies surrounding the invasive SLF and assisting in minimizing its agricultural and economic damage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
