Low-Shot Wall Defect Detection for Autonomous Decoration Robots Using Deep Reinforcement Learning
Wall defect detection is an important function for autonomous decoration robots. Object detection methods based on deep neural networks require a large number of images with the handcrafted bounding box for training. Nonetheless, building large datasets manually is impractical, which is time-consumi...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2020/8866406 |
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| _version_ | 1832566290767675392 |
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| author | Fanyu Zeng Xi Cai Shuzhi Sam Ge |
| author_facet | Fanyu Zeng Xi Cai Shuzhi Sam Ge |
| author_sort | Fanyu Zeng |
| collection | DOAJ |
| description | Wall defect detection is an important function for autonomous decoration robots. Object detection methods based on deep neural networks require a large number of images with the handcrafted bounding box for training. Nonetheless, building large datasets manually is impractical, which is time-consuming and labor-intensive. In this work, we solve this issue to propose the low-shot wall defect detection algorithm using deep reinforcement learning (DRL) for autonomous decoration robots. Our algorithm first utilizes the attention proposal network (APN) to generate attention regions and applies AlexNet to extract the features of attention patches to further reduce computation. Finally, we train our method with deep reinforcement learning to learn the optimal detection policy. The experiments are implemented on a low-shot dataset in which images are collected from real decoration environments, and the experimental results show the proposed method can achieve fast convergence and learn the optimal detection policy for wall defect images. |
| format | Article |
| id | doaj-art-9d3ff4f72fe64ebebc2da7f96983239b |
| institution | Kabale University |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-9d3ff4f72fe64ebebc2da7f96983239b2025-02-03T01:04:30ZengWileyJournal of Robotics1687-96001687-96192020-01-01202010.1155/2020/88664068866406Low-Shot Wall Defect Detection for Autonomous Decoration Robots Using Deep Reinforcement LearningFanyu Zeng0Xi Cai1Shuzhi Sam Ge2School of Computer Science and Engineering, Center for Robotics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, Center for Robotics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Computer Science and Engineering, Center for Robotics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaWall defect detection is an important function for autonomous decoration robots. Object detection methods based on deep neural networks require a large number of images with the handcrafted bounding box for training. Nonetheless, building large datasets manually is impractical, which is time-consuming and labor-intensive. In this work, we solve this issue to propose the low-shot wall defect detection algorithm using deep reinforcement learning (DRL) for autonomous decoration robots. Our algorithm first utilizes the attention proposal network (APN) to generate attention regions and applies AlexNet to extract the features of attention patches to further reduce computation. Finally, we train our method with deep reinforcement learning to learn the optimal detection policy. The experiments are implemented on a low-shot dataset in which images are collected from real decoration environments, and the experimental results show the proposed method can achieve fast convergence and learn the optimal detection policy for wall defect images.http://dx.doi.org/10.1155/2020/8866406 |
| spellingShingle | Fanyu Zeng Xi Cai Shuzhi Sam Ge Low-Shot Wall Defect Detection for Autonomous Decoration Robots Using Deep Reinforcement Learning Journal of Robotics |
| title | Low-Shot Wall Defect Detection for Autonomous Decoration Robots Using Deep Reinforcement Learning |
| title_full | Low-Shot Wall Defect Detection for Autonomous Decoration Robots Using Deep Reinforcement Learning |
| title_fullStr | Low-Shot Wall Defect Detection for Autonomous Decoration Robots Using Deep Reinforcement Learning |
| title_full_unstemmed | Low-Shot Wall Defect Detection for Autonomous Decoration Robots Using Deep Reinforcement Learning |
| title_short | Low-Shot Wall Defect Detection for Autonomous Decoration Robots Using Deep Reinforcement Learning |
| title_sort | low shot wall defect detection for autonomous decoration robots using deep reinforcement learning |
| url | http://dx.doi.org/10.1155/2020/8866406 |
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