The Recognition of Maize seeds Based on Multi-scale Feature Fusion and Extreme Learning Machine
Mingzhi Du, Xiao Ke, Ming-Ke Zhou · Advances in engineering research/Advances in Engineering Research · 2015
In recognizing traditional crops seeds like maize seeds, we usually use electrophoresis assay method, fluorescence scanning method and chemical assay method.These methods are destructive methods.They take a long time to detect and are demanding of professional background knowledge and hardware conditions etc. What's more, these methods, based on BP neural network and support vector machine (SVM)while taking a long time to detect are less accurate in process of classification.In this paper, based on the computer vision technology, we proposed a new method for the classification of maize seeds, a method based on multi-scale feature fusion and extreme learning machine.First, we extract the multi-scale fusion feature of maize seeds.Second, based on extreme learning machine, we construct the classifier model of maize seed.Third, because of the window of image in the case of multi-scale detection has the problem of capturing the same object seed with many overlapping windows, we put forward a kind of window fusion algorithm to solve it.The simulation results show that: The method is able to identify the maize seeds accurately.Using this method the accuracy of classification of maize seeds can reached 97.66% and the error rate is less than 0.1%.Compared with the traditional methods, the method we proposed can improve the speed of detection and the accuracy of classification, and has no strict hardware requirements.