Improved SSD Object Detection Algorithm Based on Segmented Deconvolution
MA Yue1,ZHAO Zhi-hao1,2,YIN Zhen-yu1,FAN Chao1,2,CHAI An-ying1,2,LI Cheng-meng1,2
1(Shenyang Institute of Computing Technology,Chinese Academy of Sciences,Shenyang 110168,China)2(University of Chinese Academy of Sciences,Beijing 100049,China)
Abstract：The lack of semantic information in the low-level feature maps of current SSD algorithm leads to the problem of missed detection and false detection of small objects.An SD-SSD(Segmented Deconvolutional-Single Shot MultiBox Detector)object detection algorithm based on segmented deconvolution is proposed.According to the low-level feature map of the SSD model,the semantic information extraction is insufficient.There is too much loss of detailed information in high-level feature maps.This article redesigned the fusion structure.Not only reduces the number of parameters in the calculation process,but also enriches the detailed information and semantic information of each feature map.Too many times of feature map deconvolution will increase noise information.In this paper,the high-level feature map in the model is divided into three segments for segmented deconvolution and low-level feature layer fusion.To enhance the detection effect of small objects under the model.Add lower-level feature maps for feature fusion to strengthen the detection of small objects.The algorithm is verified on the Pascal VOC2007 test set.In this paper,the SD-SSD model significantly improves the AP value of the small objects.mAP increased by 4.30% and 3.0% respectively compared to SSD and DSSD models.Compared with the current mainstream single-stage object detection algorithm,the algorithm in this paper still has higher detection accuracy and detection speed.
2021, Vol. 42 
