Chep Technology Pty Limited

Sighthound, Inc.

Gonzalo Vaca Castano

Syed Zain Masood

Christopher J Gerou

Stephen Neish

Khurram Soomro

Sergio Conejo

Moazam Soomro

A pallet inspection system includes a conveyor to move a pallet that is to be inspected. The pallet includes a top deck and a bottom deck separated by spaced apart support blocks positioned therebetween, with nails being used to secure the top and bottom decks to the support blocks. Cameras are positioned to generate images of the pallet as the pallet is moved on the conveyor. A processor is coupled to the cameras and receives the images for processing. The processing includes executing a first algorithm on the images to tag the images having support blocks visible therein, and executing a second algorithm on the tagged images to detect nails having exposed tips.

Pallet inspection system and associated methods

A Convolutional Neural Network (CNN) includes an initial set of convolutional layers and max pooling units, in which any input is convoluted with the learned image filters and the output is a stack of the different filter responses. Max pooling produces a scaled version of the output. The process can be repeated several times, resulting in a stack of space invariant-scaled images. Since the operation is space invariant, the computations of these layers not need to be recomputed if interested just in certain regions of the image. A Region Of Interest (ROI) Pooling layer is used to select regions to be processed by the set of fully connected layers, which uses the response of the multiple convolutional layers of the network to determine the regions where the objects (of different scales) could be located. This object proposal method is implemented as a Region Of Interest (ROI) Selector.

Deeply learned convolutional neural networks (CNNS) for object localization and classification

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Orlando, FL, United States of America

Gonzalo Vaca Castano