| Author: | Fang, Hong |
| Title: | One stage grasp detection with transfer learning |
| Degree: | M.Sc. |
| Year: | 2021 |
| Subject: | Robotics Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Mechanical Engineering |
| Pages: | 53 pages : color illustrations |
| Language: | English |
| Abstract: | Grasp detection plays an important role in robotic application. In recent years, due to successful utilization of deep learning in computer vision, natural language processing and autonomous driving, grasp detection with deep learning has inspired a great deal of researchers' attention. Compared with arduous and time-consuming traditional strategies applied in grasp detection, deep learning can significantly enhance the accuracy and generalization ability of robotic grasping aiming for novel objects. In this work, we propose a novel convolutional neural network architecture based on VGG-16 and follow the design ideas of YOLO to realize grasps detection aiming for five-dimensional representation. In order to adapt our training strategy for oriented bounding box in grasp detection while increasing network's regression accuracy and training speed, we propose a new angle matching mechanism to replace Jaccard Index Matching. Meanwhile, transfer learning is also used in our strategy to enhance network's ability of feature extraction and end-to-end training speed. Five-fold cross validation indicates our proposed algorithm has satisfied performance in predicting objects' grasp configurations, which achieves an accuracy of 78.7% and 77.3% in image-wise split and object-wise split respectively after only training 5 epochs. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5927.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.75 MB | Adobe PDF | View/Open |
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