Image Enhancement for Machine Vision and Industrial Image Processing
Daniel Weerts, Maren Petersen · Procedia CIRP · 2024
Machine vision systems and image processing have become an integral part of today’s production lines. The reasons for this are the high degree of flexibility and adaptability that they offer. However, the robustness of such systems is heavily dependent on stable environmental conditions such as constant lighting. The method presented here is intended to remedy this issue by using a deep learning approach to transfer the characteristics of good images to negatively affected images. In addition to changing light conditions, a possible variety of part colors is also taken into account. The approach is verified using an exemplary pick-and-place application with a smart camera. The experiment resulted in a significant improvement in the object detection task. The smart camera successfully detected objects in images where previous attempts had failed.