Abstract
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.
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