R-CNN based automated visual inspection system for engine parts quality assessment

In this paper, we attempt to answer to a quality control problem in the context of an industrial serial production of lower plates (wheel suspensions) for the automotive industry. These frame parts are produced by a 2000-ton stamping machine that can reach 1800 parts per hour. The quality of these parts is assessed by a visual quality control operation. This operation is time-consuming. Moreover, many factors can affect its performance, as the attention of the operators in charge, or a too rapid inspection completion time, and non-detection defects lead to high supplementary costs. To answer this issue and automate this process operation, a system based on a vision system coupled to a pre-trained Convolutional Neural Networks (Mask R-CNN)1 has been designed and implemented. In addition, an artificial enlargement of the reference image base is proposed to improve the robustness of the identification, and reduce the sensitivity of the results to potential imaging artefacts due to non-controlled environments factors such as overexposure, blur, shadows or oil fog.