The paper will discuss the issues with industrial vision systems in detecting defects, especially in the manufacturing of tyres, where changes in exterior light and distortion will compromise the accuracy of defect detection. The suggested scheme combines Directional Convoluted Ternary Pattern (DCTP) in the extraction of texture features and Distributed Pattern-based Neural Network (DPNN) in the classification. The DCTP model aims to complement the border detection of tyre defects, resolving the problem of illumination and noise variations. The DPNN classifier with a distributed method of learning patterns of features has a significant effect on the model complexity; the prediction accuracy is increased by fewer training samples. The model was statistically verified using sensitivity (0.9862), specificity (0.9743), precision (0.9801), recall (0.9862), F1-score (0.982), accuracy (98.28), and Kappa coefficient (0.972). The mentioned metrics demonstrate a substantial improvement over the current models, such as YOLOv5, CNN, and SVM. The suggested DCTP-DPNN architecture is better than the conventional methods with the highest classification accuracy (up to 98 %) and the lowest error (2 %). These results demonstrate that the hybrid feature extraction method on the basis of texture and classification using neural networks is good for detecting tyre defects in industries in real-time.
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