TY - GEN
T1 - Enhancement and Fusion of Multi-Scale Feature Maps for Small Object Detection
AU - Xue, Zhijun
AU - Chen, Wenjie
AU - Li, Jing
N1 - Publisher Copyright:
© 2020.
PY - 2020/7
Y1 - 2020/7
N2 - In recent years, deep convolutional neural networks have made breakthrough progress in object recognition and object detection tasks in the field of computer vision, and have achieved great results both in accuracy and speed. However, the detection of small objects is still difficult in the field of object detection, and the accuracy on the common dataset MS COCO is very low. This paper briefly reviews some work in multi-scale object detection algorithms, and then proposes a method of feature enhancement and fusion based on multi-scale feature maps, improving detection accuracy of small objects on MS COCO.
AB - In recent years, deep convolutional neural networks have made breakthrough progress in object recognition and object detection tasks in the field of computer vision, and have achieved great results both in accuracy and speed. However, the detection of small objects is still difficult in the field of object detection, and the accuracy on the common dataset MS COCO is very low. This paper briefly reviews some work in multi-scale object detection algorithms, and then proposes a method of feature enhancement and fusion based on multi-scale feature maps, improving detection accuracy of small objects on MS COCO.
KW - Feature Enhancement and Fusion
KW - Multi-scale
KW - Small Object Detection
UR - http://www.scopus.com/inward/record.url?scp=85091397456&partnerID=8YFLogxK
U2 - 10.23919/CCC50068.2020.9189352
DO - 10.23919/CCC50068.2020.9189352
M3 - Conference contribution
AN - SCOPUS:85091397456
T3 - Chinese Control Conference, CCC
SP - 7212
EP - 7217
BT - Proceedings of the 39th Chinese Control Conference, CCC 2020
A2 - Fu, Jun
A2 - Sun, Jian
PB - IEEE Computer Society
T2 - 39th Chinese Control Conference, CCC 2020
Y2 - 27 July 2020 through 29 July 2020
ER -