Real-time tool detection in smart manufacturing using You-Only-Look-Once (YOLO)v5

Abstract

Computer vision plays an essential role in Industry 4.0 by enabling machinery to perceive, analyze, and control production processes. Object detection, a computer vision technique that accurately classifies and localizes objects within images, has gained significant interest. This technique can be applied in various domains, including manufacturing, to assist in the detection of different tools. In this paper, You-Only-Look-Once (YOLO)v5 real-time object detection technique has been developed and optimized, to detect different tool types and their locations in a manufacturing setting. To train the neural network, a dataset of 3,286 tool images from the internet has been collected and annotated. To enhance the model's ability in generalization, three augmented variants of each image have been created to improve rotation invariance. The model's training scheme has been further optimized with stochastic gradient descent after configuring different hyperparameters such as learning rate and momentum. The fine-tuned model achieved a mean average accuracy of 98.3 %, demonstrating the high precision of the model in detecting different tool types and their locations in real-time.