Open AccessRetracted: KSSD: single‐stage multi‐object detection algorithm with higher accuracy
Author(s) -
Hou Qingshan,
Xing Jinsheng
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2020.0077
Subject(s) - object detection , computer science , stability (learning theory) , detector , algorithm , pattern recognition (psychology) , single stage , object (grammar) , artificial intelligence , single shot , function (biology) , class (philosophy) , regression , process (computing) , mathematics , statistics , machine learning , telecommunications , physics , optics , evolutionary biology , engineering , biology , aerospace engineering , operating system
Considering that the single shot multibox detector (SSD) algorithm will be missed or even false when is used to detect the small‐ and medium‐sized objects, in this study, Kullback–Leibler single shot multibox detection (KSSD) object detection algorithm is proposed to improve the accuracy of small‐ and medium‐sized objects detection. Firstly, the details in the detection process are visualised with gradient‐weighted class activation mapping technology, and the details of each detection layer are shown in the form of class activation maps. Then it is noted that the phenomenon of the false or missed detection of the objects to be detected on small‐ and medium‐sized objects in the SSD algorithm is related to the regression loss function. Accordingly, Kullback–Leibler border regression loss strategy is adopted and non‐maximum suppression algorithm is used to output the final prediction boxes. Experimental results show that compared with the existed detection algorithms, the improved algorithm in this study has higher accuracy and stability, and can significantly improve the detection effect on small‐ and medium‐sized objects.