Insect Detection Research in Natural Environment Based on Faster-R-CNN Model

Abstract

In recent years, image-based automatic insect target detection technology has been developed in the field of insect target detection. Traditional insect target detection is mainly artificial identification, but in order to avoid the problem of low detection accuracy caused by subjective factors, using convolutional neural network to extract features automatically and using the deep learning model to detect insect targets. In addition, we improve the model from the following two aspects: On the one hand, because most of insect data sets we collected are taken in the field, the background of the data sets is very complex and the image resolution is not high. For this reason, we replace the basic network VGG16 of the model with ResNet50 with a deeper layer of network structure and fewer parameters. On the other hand, we use OHEM (online hard example mining) to solve the imbalance between the target frame and background frame in target detection. The results show that the accuracy of the improved Faster-RCNN model is 89.64, which is 4.31% higher than that of the non improved Faster-RCNN model.