基于轻量级神经网络的2种害虫钻蛀振动识别方法

Objective: In order to early warn stealthy wood boring pest, lightweight neural networks were designed and implemented in the present study, and the acoustics recognition technique was used to automatically identify larvae boring vibrations of Semanotus bifasciatus (Coleoptera: Cerambycidae) and Eucryptorrhynchus brandti (Coleoptera: Curculionidae).Method: The SP-1L piezoelectric vibration probe of the AED-2010L sound pick-up was embeded into the woods sections with larvae of S.bifasciatus and E.brandti, the boring vibrations were recorded by the sound recorder. The sounds of S.bifasciatus, E.brandti and no insect were preprocessed by endpoint detection algorithm and time warping algorithm to calculate the log mel-spectrograms. The log mel-spectrograms were then fed into the convolutional neural networks (CNNs). The boring vibrations were short impulses, thus their size was far smaller than that of images. In this paper the InsectFrames of a light-weight CNN were designed in order to prevent over-fitting of the mode. The insectFrames consisted of four convolutional layers using 3×3 kernels. Global average pooling was located before the connected layer, which can reduce parameters of network.The present study proposed four different CNNs which named InsectFrames_1-4 based on different feature interlayer dimensions and dimensionality reduction method.Result: The vibration identification method for identifying the two insect boring vibrations based on neural networks was able to efficently monitor the occurrence of forest boring insects early, and accurately identify the pest species.The sounds of S.bifasciatus, E.brandti and no insect were recognized using InsectFrames_1-4.The average accuracy on the test set reached more than 90%, and the average recognition time of CPU was about 0.1-1.3 s. Among them, model InsectFrame-2 was the best and its accuracy was 95.83%, and the accuracy was 34.2% higher compared to the Gaussian mixture model which is widely used for insect sound recognition.The accuracy increased by 6.94% compared to the heavy-weight ResNet18 model designed for image classification, and the time efficiency increased by more than 170 times.Conclusion: The proposed method applies neural network and sound identification technique to automatic monitoring of larvae boring vibrations, which can improve the early warning of forestry boring insects, and has the advantages of high efficiency, simplicity and low cost.