Defect detection in pattern texture analysis using improved support vector machine

Manimozhi, I.; Janakiraman, S.

doi:10.1007/s10586-018-2551-y

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…The classification In this Table 4, it is worth noting that the classification effects based on GLRLM and LBP features are very weak. In previous studies, when using GLRLM texture features for classification tasks, there is good performance [41,42]. Khojastehnazhand et al [43] used GLRLM and GLCM features to classify raisins and found that the classification effect of GLRLM was better than GLCM features.…”

Section: Classification Mode and Classifier Performancementioning

confidence: 99%

Cotton stubble detection based on wavelet decomposition and texture features

et al. 2021

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Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. Methods Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. Results The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. Conclusions The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.

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Section: Classification Mode and Classifier Performancementioning

confidence: 99%