Automatic Recognition System For Mechanical Hand Tools Using Convolutional Neural Networks

Abstract

the identification of high-precision mechanical tools is an important problem faces mechanical engineers. Indeed, hundreds of different instruments are typically used for one task. In several cases and multiple workshop environments each tool will be used. Mechanical engineering is a practical field that need different mechanical tools during workshop work.  These tools such as Wrench, hammer, toolbox, Gasoline Can, and pebble have different size and style. During work time of the mechanical engineers, they need these tools frequently and the identification process of these tools is a difficult task for automated system. In this paper, an automated recognition system for mechanical tools using convolution neural networks. A CNN-based model is discussed using four versions of residual network classifiers; ResNet-18, ResNet-34, ResNet-50 and ResNet-152. This model can be integrated with a robot to give it the ability to recognize the specific mechanical tool and deliver it to the mechanical engineer. The feasibility of this method is illustrated in the achieved results. The obtained results are very promising to be used in practical use. In term of testing accuracy, the results achieved 84%, 85%, 86% and 87% for ResNet18, ResNet-34, ResNet-50 and ResNet-152 respectively.