[1]HONG Kailin,CAO Jiangtao,JI Xiaofei.Indoor window detection of autonomous spraying robot based on improved CenterNet network[J].CAAI Transactions on Intelligent Systems,2021,16(3):425-432.[doi:10.11992/tis.202005016]
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Indoor window detection of autonomous spraying robot based on improved CenterNet network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 3 Page number: 425-432 Column: 学术论文—机器感知与模式识别 Public date: 2021-05-05
Title:
Indoor window detection of autonomous spraying robot based on improved CenterNet network
Author(s):
HONG Kailin1;  CAO Jiangtao1;  JI Xiaofei2
1. School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, China;
2. School of Automation, Shenyang Aerospace University, Shenyang 110136, China
Keywords:
spraying robot; deep learning; target detection; indoor window detection; Center-Net; Ghost block; attention mechanism; embedded device
CLC:
TP391.1
DOI:
10.11992/tis.202005016
Abstract:
An indoor autonomous spraying robot can realize the automation of indoor wall spraying to improve the efficiency of spraying and reduce the investment of manpower and material resources. The indoor window detection algorithm based on computer vision is the key technology of the robot. For indoor window detection, traditional methods cannot obtain good results owing to the actual scene’s requirements for recognition speed and accuracy as well as the presence of lighting in the environment, shape of the window, and light transmission properties of the window. To solve this problem, an indoor window detection algorithm based on deep learning is designed. This algorithm mainly modifies the backbone feature extraction of the CenterNet network, reduces part of the convolution operation, replaces the original convolution module with ghost block, reduces the redundancy feature, and introduces an attention mechanism to keep the network under a limited number of parameters that express important information as much as possible. The experimental results show that the improved CenterNet algorithm greatly improves the operation speed of the network without losing the accuracy of the network so that the network can achieve a real-time detection effect even on the embedded system of the robot.
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