Improved Feature Fusion Network for Small Object Detection in Remote Sensing Images

被引：0

作者：

Li, Chao ^{[1
]}

Wang, Kai ^{[1
]}

Ding, Caichang ^{[2
]}

Zhang, Jinyue ^{[1
]}

Li, Jiabao ^{[1
]}

机构：

[1] School of Computer, Hubei University of Technology, Wuhan

[2] School of Computer and Information Science, Hubei Engineering University, Hubei, Xiaogan

来源：

Computer Engineering and Applications | 2023年 / 59卷 / 17期

关键词：

attention mechanism; feature fusion; remote sensing image; small object detection;

D O I：

10.3778/j.issn.1002-8331.2205-0058

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Small scale and dense arrangement of objects in remote sensing images present significant challenges for feature extraction and object detection. To solve the challenges above, a network based on feature fusion and attention mechanism is proposed. Firstly, to enhance detailed features which are useful for small scale object, a scale attention module is proposed to filter invalid semantic features and generate attention masks with different scales after the backbone network. Moreover, an edge refinement module is introduced to reduce false detection by suppressing feature misalignment in the dense area. Experimental results show that, compared with the baseline model Faster R- CNN, the method improves the detection accuracy AP50 and APS by 10 and 11.1 percentage points, respectively. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：232 / 241

页数：9

共 29 条

[1]

GAO X B, MO M J C，, WANG H T，, Et al., Recent advances in small object detection[J], Journal of Data Acquisition and Processing, 36, 3, pp. 391-417, (2021)

[2]

GIRSHICK R, DONAHUE J, DARRELL T, Et al., Rich feature hierarchies for accurate object detection and semantic segmentation[C], Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, (2014)

[3]

HE K, ZHANG X, REN S，, Et al., Spatial pyramid pooling in deep convolutional networks for visual recognition[J], IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 9, pp. 1904-1916, (2015)

[4]

GIRSHICK R., Fast R-CNN[C], Proceedings of the IEEE International Conference on Computer Vision, pp. 1440-1448, (2015)

[5]

REN S, HE K, GIRSHICK R, Et al., Faster R- CNN ：towards real- time object detection with region proposal networks[C], Advances in Neural Information Processing Systems, (2015)

[6]

EVERINGHAM M, ESLAMI S M, VAN GOOL L, Et al., The pascal visual object classes challenge：a retrospective[J], International Journal of Computer Vision, 111, 1, pp. 98-136, (2015)

[7]

LIN T Y, MAIRE M, BELONGIE S, Et al., Microsoft COCO：common objects in context[C], European Conference on Computer Vision, pp. 740-755, (2014)

[8]

LIN T Y, DOLLAR P, Et al., Feature pyramid networks for object detection[C], Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117-2125, (2017)

[9]

YU X, GONG Y, JIANG N, Et al., Scale match for tiny person detection[C], Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1257-1265, (2020)

[10]

ZOPH B, CUBUK E D, GHIASI G, Et al., Learning data augmentation strategies for object detection[C], European Conference on Computer Vision, pp. 566-583, (2020)

← 1 2 3 →