Open AccessTarget part detection based on improved SSD algorithm
Author(s) -
Yongwei Yu,
Xin Han,
Liuqing Du
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1486/3/032024
Subject(s) - normalization (sociology) , computer science , residual , algorithm , block (permutation group theory) , artificial intelligence , process (computing) , mathematics , geometry , sociology , anthropology , operating system
Aiming at the problem that the traditional target detection algorithm cannot balance the target detection accuracy and real-time detection, and the detection effect is poor under the actual complex production conditions. A deep learning detection method based on Inception-SSD framework is proposed. The Inception-SSD, which integrates the Inception prediction structure, introduces the Inception block into the extra layer of the SSD network. Then it uses batch normalization (BN) and residual structure connection to capture more target information without increasing the complexity of the network. It will improve its accuracy without affecting its real-time detection. The result of the experiment show that the detection accuracy of this model is 97.8% in the actual production process, which is largely improved comparing with the original SSD algorithm. The detection time is 48ms, which improves the detection accuracy and ensures the real-time performance. It can meet the detection requirements of parts in actual production.