Abstract
Deep learning is applied in various manufacturing domains. To train a deep learning network, we must collect a sufficient amount of training data. However, it is difficult to collect image datasets required to train the networks to perform object recognition, especially because target items that are to be classified are generally excluded from existing databases, and the manual collection of images poses certain limitations. Therefore, to overcome the data deficiency that is present in many domains including manufacturing, we propose a method of generating new training images via image pre-processing steps, background elimination, target extraction while maintaining the ratio of the object size in the original image, color perturbation considering the predefined similarity between the original and generated images, geometric transformations, and transfer learning. Specifically, to demonstrate color perturbation and geometric transformations, we compare and analyze the experiments of each color space and each geometric transformation. The experimental results show that the proposed method can effectively augment the original data, correctly classify similar items, and improve the image classification accuracy. In addition, it also demonstrates that the effective data augmentation method is crucial when the amount of training data is small.
Highlights
In smart manufacturing, enormous amounts of data are acquired from sensors
The proposed method is divided into three parts: image pre-processing, color perturbation via a random Gaussian distribution based on the inverse peak signal-to-noise ratio (PSNR), and geometric transformations
We proposed a method to improve the image classification accuracy for small datasets
Summary
Enormous amounts of data are acquired from sensors. The complexity of data poses challenges when processing these data, such as a high computational time. We must collect enormous amounts of data to train the deep learning network and apply it to object recognition, as the classification accuracy is related to the amount of training data. To enhance the performance of deep learning, we must collect numerous training datasets This is because the classification result is influenced by the number of training images. The overall research objective is to recognize and classify ten components for picking, placing, and assembling work This yields data deficiency when training the deep learning network to recognize and classify components because these components are uncommon, as previously mentioned. We propose a method for effectively improving the object classification accuracy of the deep learning network in the case of a small dataset in various manufacturing domains.
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