Considering that the single shot multibox detector (SSD) algorithm will be missed or even false when it is used to detect the small and medium-sized objects

an improved SSD object detection algorithm is proposed to improve the accuracy of small and medium-sized objects detection. The details in the detection process are visualized with gradient-weighted class activation mapping (Grad-CAM) 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 (KL) border regression loss strategy is adopted and non maximum suppression (NMS) algorithm is used to output the final prediction boxes. Experimental results show that compared with the existing detection algorithms

the improved algorithm in this paper has higher accuracy and stability.