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

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.

Deep Machine Learning of MobileNet, Efficient, and Inception Models

“…In paper [18], it was shown that it is possible to recognise knife-type hand-held weapons carried by armed individuals from digital images with an accuracy of 87%. Study [19] used synthetic and real images applied to CNNs to classify warehouse items. The AI model was based on a combination of DenseNet and Resnet pipelines for colour and depth images and proved outperformance in terms of accuracy and precision rates compared to single CNNs, achieving a 95.23% accuracy.…”

Early Fault Diagnosis of Rolling Bearing Based on Threshold Acquisition U-Net

“…Convolution neural network (CNN), a deep neural network among many deep neural networks, is very popular because of its sparse connection and weight-sharing feature [21]. Notably, CNNs [22,23] have brought many significant breakthroughs in image recognition. Inspired by CNNs in image recognition, Ronneberge et al [24] proposed a fully convolutional neural network, U-Net, with an encoder-decoder architecture for medical image segmentation.…”