RETRACTED: KSSD: single-stage multi-object detection algorithm with higher accuracy (Retracted article. See vol. 17, pg. 301, 2023)

作     者：Hou, Qingshan Xing, Jinsheng 

作者机构：Shanxi Normal Univ Sch Math & Comp Sci Linfen Shanxi Peoples R China 

出 版 物：《IET IMAGE PROCESSING》 (IET Image Proc.)

年 卷 期：2020年第14卷第15期

页      面：3651-3661页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Soft Science Foundation of Shanxi Province [2011041033-03] 

主　　题：object detection KSSD multiobject detection algorithm single shot multibox detector algorithm Kullback-Leibler single shot multibox detection medium-sized objects detection detection process gradient-weighted class activation mapping technology detection layer class activation maps false missed detection SSD algorithm regression loss function Kullback-Leibler border regression loss strategy nonmaximum suppression algorithm detection effect medium-sized objects small-sized objects 

摘      要：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.