A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning

Abstract

With increasing interest in smart factories, considerable attention has been paid to the development of deep-learning-based quality inspection systems. Deep-learning-based quality inspection helps productivity improvements by solving the limitations of existing quality inspection methods (e.g., an inspector’s human errors, various defects, and so on). In this study, we propose an optimized YOLO (You Only Look Once) v5-based model for inspecting small coils. Performance improvement techniques (model structure modification, model scaling, pruning) are applied for model optimization. Furthermore, the model is prepared by adding data applied with histogram equalization to improve model performance. Compared with the base model, the proposed YOLOv5 model takes nearly half the time for coil inspection and improves the accuracy of inspection by up to approximately 1.6%, thereby enhancing the reliability and productivity of the final products.

Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery

“…The neural network's design is implemented through a feedforward network, where information flows from the input layer to the hidden layers, ultimately reaching the output layer. This means that object detection for the entire image is performed in a single pass of the algorithm, simultaneously predicting the probability of an object belonging to a certain class and the bounding boxes that specify its location in the image (Figure 4) [77]. The YOLO algorithm family consists of multiple models, with YOLOv5 being easy to train with good reliability and stability [78].…”