LAUSR

The detection and identification of stored grain insects is important to ensure the safety of grain during grain storage. At present, insect identification methods primarily rely on manual classification; therefore, the automatic, rapid and accurate detection of stored grain insects remains a challenge. This paper proposes an improved detection neural network architecture based on R-FCN to solve the problem of detection and classification of eight common stored grain insects. In this network, we use the multiscale training strategy with a fully convolutional network to extract more features of the insects and automatically provide the location of potentially stored grain insects through an RPN from the feature map. By using the position-sensitive score map to replace some fully-connected layers, our network is more adaptive to detect insects in complicated backgrounds, and our detection speed is improved. In addition, we also used soft-NMS to solve the superposition interference between insects and to further improve the detection accuracy. Sufficient comparative experiments are performed using our two stored grain insect detection datasets, which are carefully annotated by entomologists. Quantitative comparisons against several prior state-of-the-art methods demonstrate the superiority of our approach. Experimental results show that the proposed method achieves a higher accuracy and is faster than the state-of-the-art insect image classification algorithms.