用视觉变压器+卷积门控循环单元，精准识别登革热蚊子运动异常。
A Hybrid Framework of Vision Transformer and Gated Recurrent Unit for Detection of Mosquito Diseases
- 先用YOLO 11M定位蚊子并去背景，再用ViT提取空间特征。
- ConvGRU模型达88.88%准确率，有效捕捉运动时序依赖性。
- 适合研究昆虫行为或复杂场景视频分析的科研人员使用。
由于蚊子体型微小且视频背景复杂，区分登革热病毒感染蚊子与健康蚊子在运动行为分析中面临挑战。传统人工智能方法难以从视频帧中提取准确特征，常产生误判。本研究提出三步框架：首先利用YOLO 11M模型识别蚊子并去除背景；其次通过视觉变换器（ViT）提取视觉特征；最后采用卷积门控循环单元（ConvGRU）进行视频分类。对比RNN、LSTM、GRU及其卷积版本，ConvGRU表现最优，达到88.88%准确率、84.45%精确率、82.82%召回率和82.81%F1分数。结果表明，将卷积模型与序列网络结合，尤其在ConvGRU中，可同时捕获蚊子运动的精确空间特征与长期时间依赖性。该框架为复杂环境中蚊子行为分析提供了可靠解决方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。