A Low-Cost Location System Based on Deep Learning for Stacked Materials Sorting

To improve industrial production efficiency, a low-cost location system based on deep learning was proposed for stacked materials sorting. Firstly, taking the images got from monocular optical cameras as the input data, a one-stage detection method was used to obtain the candidate objects. Then, a deep convolution neural network was used to classify the adjacent ROIs of objects to filter the possible candidates. Finally, some interest images of the filtered candidates were processed to get the key shape and location of the object. Since the robustness of the algorithm and without the use of the expensive depth camera, the new method can reduce hardware cost and improve detection accuracy.The test results in the real scene show that the location error of the new system can be reduced to less than 0.3 cm.