PRELIMINARY STUDY ON EGG RECOGNITION MODEL OF AEDES ALBOPICTUS BASED ON DEEP LEARNING
Objective In this study,a pictorial database of Aedes albopictus eggs was built to establish a model for the automatic recognition and counting of eggs of this mosquito species.Methods In total,449 images of Ae.albopictus eggs from field strains in three districts of Shanghai and laboratory strains were collected.The eggs were manually calibrated using the Python environment labeling library,and the faster region-based convolution neural network(Faster R-CNN)was used to train the model with the tile overlap method.Results The results of model validation evaluation using accuracy,recall,and F-measure showed that after 15 training sessions,the loss gradually decreased with increasing training frequency and ultimately decreased to 0.000119.The mean accuracy(mAP)increased from 0.968 to 0.980 with increasing training frequency.The final model had an accuracy of up to 0.90,a recall rate of 0.97,and an F-measure of 0.93.Conclusion The established model achieved its function of assisting in egg identification and counting.With further optimization of the model and refining of its classification and recognition capabilities,it will serve as a simple and efficient auxiliary monitoring tool.
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