Method for pests detecting in stored grain based on spectral residual saliency edge detection

Abstract

Pests detecting is an important research subject in grain storage field. In the past decades, many edge detection methods have been applied to the edge detection of stored grain pests. Although some of them can realize the stored grain pests detecting, precision and robustness are not good enough. Spectral residual (SR) saliency edge detection defines the logarithmic spectrum of image as novelty part of the image information. The remaining spectrum is converted to the airspace to obtain edge detection results. SR algorithm is completely based on frequency domain processing. It not only can effectively simplify the target detection algorithm, but also can improve the effectiveness of target recognition. The experimental results show that the edge results of stored grain pests detected by SR method are effective and stable.