A PEST ACCURATE SEGMENTATION METHOD BASED ON CRITICAL POINT NONLINEAR ENHANCEMENT

Abstract

The core of intelligent and accurate plant protection of pests is the accurate identification of pest monitoring and early warning model, and the quality of pest sample image is crucial to the model identification accuracy. To solve the problem of complicated background and low contrast colour image samples, in this paper it is proposed a pest accurate segmentation method based on critical point nonlinear enhancement. The segmented image is used as the sample image of the Faster R-CNN model, which can improve the accuracy of the recognition model. Firstly, the original image is segmented by a strong classifier and the image of pest cells with calibrated grids is obtained. Secondly, the Spline adjustment curve is fitted according to the core gray scale range and critical point, and the contrast between pest and mesh in pest monomer image is enhanced based on the Spline adjustment curve. Finally, there are some operations for the enhanced image such as threshold segmentation, contour extraction, morphological transformation and others to obtain the pest image without background interference, and some segmentation experiments are performed to the pest image based on different segmentation methods. The experimental results show that the proposed method can accurately segment the pests in complex background, and the comprehensive evaluation indexes such as recall ratio and precision rate are greater than or equal to 91.5%, which is better than the traditional segmentation method.