用于蚊子疾病检测的视觉变换器与门控循环单元混合框架
A Hybrid Framework of Vision Transformer and Gated Recurrent Unit for Detection of Mosquito Diseases
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中文总结 AI 辅助
该研究针对复杂环境下蚊子疾病检测难题，提出YOLO 11M+ViT+ConvGRU的混合框架，经对比多种模型，ConvGRU取得最优性能，为蚊子行为分析提供可靠方案。
中文摘要 AI 辅助
由于蚊子体型小且视频背景复杂，从对照蚊子中识别登革病毒感染蚊子是分析蚊子运动行为的一项重大挑战。传统AI方法往往无法从视频帧中提取准确特征，会产生错误特征。本研究提出了一个三步框架：首先使用YOLO 11M模型识别蚊子并去除背景，然后使用视觉变换器（Vision Transformer, ViT）提取视觉特征，最后使用卷积门控循环单元（convolutional GRU, ConvGRU）分类器对视频进行分类。对不同模型（包括循环神经网络（Recurrent Neural Network, RNN）、长短期记忆网络（Long Short-Term Memory, LSTM）、门控循环单元（Gated Recurrent Unit, GRU）及其卷积版本）的对比分析表明，ConvGRU模型表现最佳；其准确率达88.88%，精确率达84.45%，召回率达82.82%，F1分数达82.81%。这些结果表明，将卷积模型与基于序列的网络相结合，尤其是ConvGRU模型，可同时从蚊子运动中提取精确的空间特征和长期时间依赖关系。最后，所提出的框架为复杂环境下的蚊子行为分析提供了可靠解决方案。
英文摘要
Identifying dengue virus-infected mosquitoes from control mosquitoes is a major challenge in analyzing mosquito locomotion behavior due to the small size and complexity of the video background. Conventional AI methods are often unable to extract accurate features from video frames and produce erroneous features. In this study, a three-step framework is introduced: first, mosquitoes are identified and the background is removed using the YOLO 11M model, then visual features are extracted using the Vision Transformer (ViT), and finally the videos are classified with a convolutional GRU (ConvGRU) classifier. A comparative analysis of different models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and their convolutional versions showed that the ConvGRU model achieved the best performance; it achieved 88.88% accuracy, 84.45% precision, 82.82% recall, and 82.81% F1 score. These results demonstrate that combining convolutional models with sequence-based networks, especially in the ConvGRU model, allows the simultaneous extraction of precise spatial features and long-term temporal dependencies from mosquito movements. Finally, the proposed framework provides a reliable solution for analyzing mosquito behavior in complex environments.
