*Article* **Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data †**

**Paulo Henrique Martinez Piratelo 1,2, Rodrigo Negri de Azeredo 1, Eduardo Massashi Yamao 1, Jose Francisco Bianchi Filho 2,3, Gabriel Maidl 1, Felipe Silveira Marques Lisboa 1, Laercio Pereira de Jesus 4, Renato de Arruda Penteado Neto 1, Leandro dos Santos Coelho 2,5,\* and Gideon Villar Leandro <sup>2</sup>**


**Abstract:** Electric companies face flow control and inventory obstacles such as reliability, outlays, and time-consuming tasks. Convolutional Neural Networks (CNNs) combined with computational vision approaches can process image classification in warehouse management applications to tackle this problem. This study uses synthetic and real images applied to CNNs to deal with classification of inventory items. The results are compared to seek the neural networks that better suit this application. The methodology consists of fine-tuning several CNNs on Red–Green–Blue (RBG) and Red–Green– Blue-Depth (RGB-D) synthetic and real datasets, using the best architecture of each domain in a blended ensemble approach. The proposed blended ensemble approach was not yet explored in such an application, using RGB and RGB-D data, from synthetic and real domains. The use of a synthetic dataset improved accuracy, precision, recall and f1-score in comparison with models trained only on the real domain. Moreover, the use of a blend of DenseNet and Resnet pipelines for colored and depth images proved to outperform accuracy, precision and f1-score performance indicators over single CNNs, achieving an accuracy measurement of 95.23%. The classification task is a real logistics engineering problem handled by computer vision and artificial intelligence, making full use of RGB and RGB-D images of synthetic and real domains, applied in an approach of blended CNN pipelines.

**Keywords:** convolutional neural networks; warehouse management; image classification; ensemble learning; synthetic data; depth image; electrical maintenance
