An improved SSD for small target detection
Li X(李翔)1,2,3,4,5; Luo HB(罗海波)1,2,3,4,5
2021
会议日期January 8-10, 2021
会议地点Zhuhai, China
关键词Convolution neural network Small targets SSD Target detection
页码7-11
英文摘要SSD is one of heuristic one-stage target detection approaches. Although it has got impressive results in general target detection, it still struggles in small-size object detection and precise location. In this paper, we proposed an improved SSD which forces on the small-size target detection. We include a shallow and high resolution feature into the hierarchical detection feature which are used for prediction. Then, we fuse the detection features (including the shallow and high resolution one) as a feature pyramid through some convolution layers and unsample operations to pass information from deep features to the shallow ones, aiming to enrich the semantic information of the shallow features. To make the network easier to converge, we add a L2 normalization to the bottom detection feature of the feature pyramid to make a norm balance between each pyramid feature. The experimental results on the VEDAI dataset show that the proposed method has obtained impressive progress than the original SSD for the small targets detection.
产权排序1
会议录2021 6th International Conference on Multimedia and Image Processing, ICMIP 2021
会议录出版者ACM
会议录出版地New York
语种英语
ISSN号978-1-4503-8916-7
内容类型会议论文
源URL[http://ir.sia.cn/handle/173321/29155]  
专题沈阳自动化研究所_光电信息技术研究室
通讯作者Li X(李翔)
作者单位1.The Key Laboratory of Image Understanding and Computer Vision, Shenyang, Liaoning, 110016, China
2.Key Laboratory of Opt-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, 110016, China
3.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China
4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, 110169, China
5.University of Chinese Academy of Sciences, Beijing, 100049, China
推荐引用方式
GB/T 7714
Li X,Luo HB. An improved SSD for small target detection[C]. 见:. Zhuhai, China. January 8-10, 2021.
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