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http://dx.doi.org/10.7236/JIIBC.2021.21.3.81

Apple Detection Algorithm based on an Improved SSD  

Ding, Xilong (Weifang University of Science and Technology)
Li, Qiutan (Weifang University of Science and Technology)
Wang, Xufei (Weifang University of Science and Technology)
Chen, Le (Weifang University of Science and Technology)
Son, Jinku (Weifang University of Science and Technology)
Song, Jeong-Young (Dept: Computer engineering, Pai Chai University)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.21, no.3, 2021 , pp. 81-89 More about this Journal
Abstract
Under natural conditions, Apple detection has the problems of occlusion and small object detection difficulties. This paper proposes an improved model based on SSD. The SSD backbone network VGG16 is replaced with the ResNet50 network model, and the receptive field structure RFB structure is introduced. The RFB model amplifies the feature information of small objects and improves the detection accuracy of small objects. Combined with the attention mechanism (SE) to filter out the information that needs to be retained, the semantic information of the detection objectis enhanced. An improved SSD algorithm is trained on the VOC2007 data set. Compared with SSD, the improved algorithm has increased the accuracy of occlusion and small object detection by 3.4% and 3.9%. The algorithm has improved the false detection rate and missed detection rate. The improved algorithm proposed in this paper has higher efficiency.
Keywords
RFB; Attention Model; SSD; Apple detection; Objection detection; CNN;
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