Lee Sangjun, Kim Hangi, Cho Byoung-Kwan
Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon 34134, Republic of Korea.
Department of Smart Agricultural System, Chungnam National University, Daejeon 34134, Republic of Korea.
Insects. 2023 Jun 5;14(6):526. doi: 10.3390/insects14060526.
Mosquitoes are one of the deadliest insects, causing harm to humans worldwide. Preemptive prevention and forecasting are important to prevent mosquito-borne diseases. However, current mosquito identification is mostly conducted manually, which consumes time, wastes labor, and causes human error. In this study, we developed an automatic image analysis method to identify mosquito species using a deep learning-based object detection technique. Color and fluorescence images of live mosquitoes were acquired using a mosquito capture device and were used to develop a deep learning-based object detection model. Among the deep learning-based object identification models, the combination of a swine transformer and a faster region-convolutional neural network model demonstrated the best performance, with a 91.7% F1-score. This indicates that the proposed automatic identification method can be rapidly applied for efficient analysis of species and populations of vector-borne mosquitoes with reduced labor in the field.
蚊子是最致命的昆虫之一，在全球范围内对人类造成危害。预防性预防和预测对于预防蚊媒疾病很重要。然而，目前的蚊子识别大多是人工进行的，这既耗时又费力，还会导致人为错误。在本研究中，我们开发了一种自动图像分析方法，使用基于深度学习的目标检测技术来识别蚊子种类。使用捕蚊设备采集活蚊子的彩色和荧光图像，并用于开发基于深度学习的目标检测模型。在基于深度学习的目标识别模型中，猪Transformer和更快的区域卷积神经网络模型的组合表现最佳，F1分数为91.7%。这表明所提出的自动识别方法可以快速应用于高效分析媒介传播蚊子的种类和种群，减少野外劳动力。
