| dc.description.abstract | Manufacturing is one of the principal parts of business which has been supporting the world’s economy for a long time. Currently, there are many types of manufacturers running under traditional or modern environments. Manufactures which utilize traditional environments tend to use human resources more to execute its operations, such as moving, checking, or producing items. Besides, modern ones tend to use robots or machines more than the traditional ones and many operations are done automatically without the help of humans. Thus it helps to increase the effectiveness of a manufacture’s operations as well as the income.
Though some newly built factories, use smart digital devices, traditional ones still have manual switches, lights, numerical displays, even pointer-type dashboard. So, such companies can not transmit digitized signal or values on the internet. Also, since they need workers to check status or data values of certain types of equipment, it may waste human resources, time, as well as cost. Therefore, to overcome these issues, an automated system can be implemented. Automated systems can help traditional manufacturing plants to get greater operational efficiency and make a higher income. Consequently, their competitiveness can be upgraded as they go towards the era of Industry 4.0. Based on the above statements, we propose several systems to support manufacturing environments based on artificial intelligence (AI) and deep learning in order to upgrade the environments themselves and escalate the effectiveness of the operations and manufacturing’s gain. The systems include automated image recognition, product’s defect detection, fire and/or smoke detection, and water detection. All of the systems are automated systems, done and operated by AI.
This work proposes an improved model for multi-scale defect image detection. We modified the algorithm of SSD 512 since it can detect higher-dimensional images. The proposed system makes model adjustments for images of different sizes and adjusts it fit the model suitable for automated detection. | en_US |