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浙江大学学报(工学版)
浙江大学学报(工学版)  2020, Vol. 54 Issue (9): 1777-1784    DOI: 10.3785/j.issn.1008-973X.2020.09.014
计算机技术     
复杂背景下的小目标检测算法
郑浦1(),白宏阳1,*(),李伟2,郭宏伟1
1. 南京理工大学 能源与动力工程学院，江苏 南京 210094
2. 中国人民解放军96037部队，陕西 宝鸡 721000
Small target detection algorithm in complex background
Pu ZHENG1(),Hong-yang BAI1,*(),Wei LI2,Hong-wei GUO1
1. School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2. 96037 PLA Troops, Baoji 721000, China
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摘要：

提出一种改进的多类别单阶检测器（SSD）算法. 借鉴特征金字塔算法的思想，将Conv4-3层的特征与Conv7、Conv3-3层的特征进行融合，同时增加融合后特征图每个位置对应的默认框数量. 在网络结构中增加裁剪-权重分配网络（SENet），对每层的特征通道进行权重分配，提升有用的特征权重并抑制无效的特征权重. 为了增强网络的泛化能力，对训练数据集进行一系列增强处理. 实验结果表明，改进后的算法在VOC数据集（07+12）上的检测效果良好，平均精度均值为80.4%，比改进前的算法提高了2.7%；在COCO数据集（2017）上的平均精度均值为42.5%，比改进前的算法提高了2.3%. 所提算法能够准确检测出不小于16×16像素的目标.
关键词： 深度学习;  目标检测;  多类别单阶检测器（SSD）算法;  特征融合;  特征增强    
Abstract:

An improved single-shot-multibox-detector (SSD) algorithm was proposed. Referring to the feature pyramid networks (FPN) algorithm, the features of the Conv4-3 layer were merged with the features of Conv7 and Conv3-3 layers, and the number of default boxes at each location in merged feature map was increased. The squeeze-and-excitation networks (SENet) was added to the network structure; the feature channels of each layer were weighted, in order to enhance the useful feature weights and suppress the invalid feature weights. A series of enhancements were performed on the training data to enhance the generalization performance of the network. The experimental results show that the improved algorithm has a better performance on the VOC (07+12) dataset; the mean average precision (mAP) value of the improved algorithm is 80.4%, which is 2.7% higher than that of the original algorithm; the mAP value of the improved algorithm on COCO dataset (2017) is 42.5%, which is 2.3% higher than that of the original algorithm. Thus, the proposed algorithm can accurately detect the target with a size of at least 16×16 pixels.
Key words: deep learning    target detection    single-shot-multibox-detector (SSD) algorithm    feature fusion    feature enhancement
收稿日期: 2019-08-28 出版日期: 2020-09-22
CLC:  TP 391  
基金资助: 国家自然科学基金资助项目（61603189）
通讯作者: 白宏阳     E-mail: 117108022106@njust.edu.cn;hongyang@njust.edu.cn
作者简介: 郑浦（1995—），男，硕士生，从事图像处理研究. orcid.org/0000-0002-3128-5219. E-mail： 117108022106@njust.edu.cn
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引用本文:

郑浦,白宏阳,李伟,郭宏伟. 复杂背景下的小目标检测算法[J]. 浙江大学学报(工学版), 2020, 54(9): 1777-1784.

Pu ZHENG,Hong-yang BAI,Wei LI,Hong-wei GUO. Small target detection algorithm in complex background. Journal of ZheJiang University (Engineering Science), 2020, 54(9): 1777-1784.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2020.09.014    或    http://www.zjujournals.com/eng/CN/Y2020/V54/I9/1777

图 1  多类别单阶检测器（SSD）网络模型示意图
图 2  SSD网络不同层的特征图输出比较
图 3  SSD网络不同通道的特征热力图
图 4  特征融合示意图
图 5  SE网络结构示意图
图 6  改进后的SSD网络结构
图 7  3种算法在不同数据集、不同类别上（瓶子、人、汽车、鸟）的准确率-召回率曲线对比
图 8  F_SE_SSD与SSD算法在VOC数据集上的检测结果对比
图 9  改进后算法（F_SE_SSD）在复杂背景下的检测效果
图 10  改进后算法（F_SE_SSD）检测小目标的效果
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