Abstract
In recent times, the application of enabling technologies such as digital shearography combined with deep learning approaches in the smart quality assessment of tires, which leads to intelligent tire manufacturing practices with automated defects detection. Digital shearography is a prominent approach that can be employed for identifying the defects in tires, usually not visible to human eyes. In this research, the bubble defects in tire shearography images are detected using a unique ensemble hybrid amalgamation of the convolutional neural networks/ConvNets with high-performance Faster Region-based convolutional neural networks. It can be noticed that the routine of region-proposal generation along with object detection is accomplished using the ConvNets. Primarily, the sliding window based ConvNets are utilized in the proposed model for dividing the input shearography images into regions, in order to identify the bubble defects. Subsequently, this is followed by implementing the Faster Region-based ConvNets for identifying the bubble defects in the tire shearography images and further, it also helps to minimize the false-positive ratio (sometimes referred to as the false alarm ratio). Moreover, it is evident from the experimental results that the proposed hybrid model offers a cent percent detection of bubble defects in the tire shearography images. Also, it can be witnessed that the false-positive ratio gets minimized to 18 percent.
Highlights
Industry 4.0 is the novel digital technology meant for industries, and this paradigm enables the communication, collection, and analysis of data through machines, thereby allowing quicker, more agile, and efficient processes for making superior quality goods with minimal expenditure.this digital industrial technology will assist in enhancing productivity, enabling industrial development, and revamping the profile of the personnel involved, thereby nurturing changes in the competence of business organizations and states
Our research primarily focuses on the deep learning-based bubble defects detection in tire shearography images, which plays a significant role in the automation of the tire manufacturing industries
Where TP stands for true positive, it represents the amount of diagnosed bubble patterns, which really possesses the bubble defects, and FP stands for false-positive and, it indicates the amount of not bubble patterns, which are wrongly diagnosed as bubble defects
Summary
Industry 4.0 is the novel digital technology meant for industries, and this paradigm enables the communication, collection, and analysis of data through machines, thereby allowing quicker, more agile, and efficient processes for making superior quality goods with minimal expenditure. This digital industrial technology will assist in enhancing productivity, enabling industrial development, and revamping the profile of the personnel involved, thereby nurturing changes in the competence of business organizations and states. Digital shearography is a laser-based measuring approach that relies on the processing of digital data, interferometry, and phase-shifting paradigm [3,4,5]
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