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Title: Deep learning for object detection and image segmentation
Authors: Tan, Yan Hwa
Keywords: Engineering::Mechanical engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Project: C086
Abstract: In recent years, the fast-moving consumer goods (FMCG) industry has shown significant interest in robot warehouse automation technology due to the increasing demand of e-commerce, fast and reliable delivery. However, it is not a simple task to pack a large variety of products according to mass customized orders. Therefore, a fully autonomous warehouse pick-and-place system is able to complete the job with ease by employing a robust vision system that reliably locates and recognizes objects from cluttered environment, different objects and self-occlusions. The aim of this project is to develop an automated solution to allow the robot to pick up the indicated object accurately from a clustered bin in bin-picking. The robot system setup consists of a UR5 robotic arm attached with a gripper and a vision camera. In the proposed approach, we segmented and labelled multiple perspective of a view using a convolutional neural network. A large amount of training data is required to train a deep neural network for segmentation. Therefore, the proposed solution used a self-supervised method to train a large dataset and at a faster speed. The Mask-R-CNN approach was also implemented to identify each item and their individual masks to achieve a higher accuracy for object detection and image segmentation.
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

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