Abstract:

The high competitiveness and rapid pace of development of the manufacturing industry requires companies to constantly improve, both in terms of efficiency and quality of production. It is these parameters that directly affect not only the reputation of the company, but also the costs it incurs, ultimately determining the price of the product on the market. Both production efficiency and product quality are affected by the speed and effectiveness of detecting manufacturing defects.

This paper presents a study to evaluate the effectiveness of detecting defective production quality in real time, using Convolutional Neural Networks (CNN), You Only Loon Once v8 (YOLOv8), and Faster R-CNN with ResNet-101 backbone. The study was carried out on the example of data for assembly lines in the automotive industry, supported by advanced techniques of pre-processing, data augmentation and knowledge transfer.

The results of the research made it possible to assess the potential of the analyzed solutions in detecting defects. The highest average precision (mAP₅₀) was achieved using YOLOv8. By using Faster R-CNN with the ResNet-101 backbone, higher precision was achieved only for small defects, but at a lower speed. When using the standard CNN neural network, the average precision was the lowest, while at the same time not providing spatial localization capability. The main barriers to implementation include computational requirements, as well as integration with programmable logic controllers (PLCs).