论文标题
蚊子值得16x16幼虫:对蚊子幼虫分类的深度学习体系结构的评估
A Mosquito is Worth 16x16 Larvae: Evaluation of Deep Learning Architectures for Mosquito Larvae Classification
论文作者
论文摘要
蚊子传播的疾病(MBD),例如登革热病毒,基孔肯雅病毒和西尼罗河病毒,每年在全球造成超过100万人死亡。由于许多这样的疾病被伊蚊和库尔克斯蚊子传播,因此跟踪这些幼虫对于缓解MBD的传播至关重要。即使公民科学发展并获得了较大的蚊子图像数据集,蚊子图像的手动注释变得越来越耗时且效率低下。先前的研究使用计算机视觉识别蚊子物种,卷积神经网络(CNN)已成为图像分类的事实。但是,这些模型通常需要大量的计算资源。这项研究介绍了视觉变压器(VIT)在比较研究中的应用,以改善艾edes和Culex幼虫的图像分类。在蚊子幼虫图像数据上对两个VIT模型,Vit-Base和CVT-13以及两个CNN模型进行了RESNET-18和CORVNEXT的培训,并比较确定将蚊子幼虫区分为AEDES或CULEX的最有效模型。测试表明,Convnext获得了所有分类指标的最大值,证明了其对蚊子幼虫分类的生存能力。基于这些结果,未来的研究包括通过结合CNN和Transformer架构元素来创建专门为蚊子幼虫分类设计的模型。
Mosquito-borne diseases (MBDs), such as dengue virus, chikungunya virus, and West Nile virus, cause over one million deaths globally every year. Because many such diseases are spread by the Aedes and Culex mosquitoes, tracking these larvae becomes critical in mitigating the spread of MBDs. Even as citizen science grows and obtains larger mosquito image datasets, the manual annotation of mosquito images becomes ever more time-consuming and inefficient. Previous research has used computer vision to identify mosquito species, and the Convolutional Neural Network (CNN) has become the de-facto for image classification. However, these models typically require substantial computational resources. This research introduces the application of the Vision Transformer (ViT) in a comparative study to improve image classification on Aedes and Culex larvae. Two ViT models, ViT-Base and CvT-13, and two CNN models, ResNet-18 and ConvNeXT, were trained on mosquito larvae image data and compared to determine the most effective model to distinguish mosquito larvae as Aedes or Culex. Testing revealed that ConvNeXT obtained the greatest values across all classification metrics, demonstrating its viability for mosquito larvae classification. Based on these results, future research includes creating a model specifically designed for mosquito larvae classification by combining elements of CNN and transformer architecture.