|
|
|
| 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 |
|
|
|
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.
|
|
Received: 28 August 2019
Published: 22 September 2020
|
|
|
|
Corresponding Authors:
Hong-yang BAI
E-mail: 117108022106@njust.edu.cn;hongyang@njust.edu.cn
|
复杂背景下的小目标检测算法
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
|
|
| [1] |
YILMAZ A, JAVED O, SHAH M Object tracking: a survey[J]. ACM Computing Surveys, 2006, 38 (4): 1- 29
|
|
|
| [2] |
李旭冬, 叶茂, 李涛 基于卷积神经网络的目标检测研究综述[J]. 计算机应用研究, 2017, 34 (10): 2881- 2886
LI Xu-dong, YE Mao, LI Tao Review of object detection based on convolutional neural networks[J]. Application Research of Computers, 2017, 34 (10): 2881- 2886
doi: 10.3969/j.issn.1001-3695.2017.10.001
|
|
|
| [3] |
周晓彦, 王珂, 李凌燕 基于深度学习的目标检测算法综述[J]. 电子测量技术, 2017, 40 (11): 89- 93
ZHOU Xiao-yan, WANG Ke, LI Ling-yan Review of object detection based on deep learning[J]. Electronic Measurement Technology, 2017, 40 (11): 89- 93
doi: 10.3969/j.issn.1002-7300.2017.11.020
|
|
|
| [4] |
GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C] // IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE, 2014: 580-587.
|
|
|
| [5] |
GIRSHICK R. Fast R-CNN [C] // IEEE Conference on Computer Vision and Pattern Recognition. Santiago: IEEE, 2015: 1440-1448.
|
|
|
| [6] |
REN S Q, HE K M, GIRSHICK R, et al Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39 (6): 1137- 1149
|
|
|
| [7] |
REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C] // IEEE Conference on Computer Vision and Pattern Recognition. Washington: IEEE, 2016: 779-788.
|
|
|
| [8] |
LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector [C] // Proceedings of European Conference on Computer Vision. Amsterdam: ECCV, 2016: 21-37.
|
|
|
| [9] |
张焕龙, 胡士强, 杨国胜 基于外观模型学习的视频目标跟踪方法综述[J]. 计算机研究与发展, 2015, 52 (1): 177- 190
ZHANG Huan-long, HU Shi-qiang, YANG Guo-sheng Video object tracking based on appearance models learning[J]. Journal of Computer Research and Development, 2015, 52 (1): 177- 190
doi: 10.7544/issn1000-1239.2015.20130995
|
|
|
| [10] |
尹宏鹏, 陈波, 柴毅, 等 基于视觉的目标检测与跟踪综述[J]. 自动化学报, 2016, 42 (10): 1466- 1489
YIN Hong-peng, CHEN Bo, CHAI Yi, et al Vision-based object detection and tracking: a review[J]. Acta Automatica Sinica, 2016, 42 (10): 1466- 1489
|
|
|
| [11] |
葛宝义, 左宪章, 胡永江 视觉目标跟踪方法研究综述[J]. 中国图象图形学报, 2018, 23 (08): 1091- 1107
GE Bao-yi, ZUO Xian-zhang, HU Yong-jiang Review of visual object tracking technology[J]. Journal of Image and Graphics, 2018, 23 (08): 1091- 1107
|
|
|
| [12] |
方路平, 何杭江, 周国民 目标检测算法研究综述[J]. 计算机工程与应用, 2018, 54 (13): 11- 18
FANG Lu-ping, HE Hang-jiang, ZHOU Guo-min Research overview of object detection methods[J]. Computer Engineering and Applications, 2018, 54 (13): 11- 18
doi: 10.3778/j.issn.1002-8331.1804-0167
|
|
|
| [13] |
朱明明, 许悦雷, 马时平, 等 基于特征融合与软判决的遥感图像飞机检测[J]. 光学学报, 2019, 39 (2): 71- 77
ZHU Ming-ming, XU Yue-lei, et al Airplane detection based on feature fusion and soft decision in remote sensing images[J]. Acta Optica Sinica, 2019, 39 (2): 71- 77
|
|
|
| [14] |
辛鹏, 许悦雷, 唐红, 等 全卷积网络多层特征融合的飞机快速检测[J]. 光学学报, 2018, 38 (3): 344- 350
XIN Peng, XU Yue-lei, TANG Hong, et al Fast airplane detection based on multi-layer feature fusion of fully convolutional networks[J]. Acta Optica Sinica, 2018, 38 (3): 344- 350
|
|
|
| [15] |
朱敏超, 冯涛, 张钰 基于FD-SSD的遥感图像多目标检测方法[J]. 计算机应用与软件, 2019, 36 (1): 232- 238
ZHU Min-chao, FENG Tao, ZHANG Yu Remote sensing image multi-target detection method based on FD-SSD[J]. Computer Applications and Software, 2019, 36 (1): 232- 238
doi: 10.3969/j.issn.1000-386x.2019.01.042
|
|
|
| [16] |
LIN T Y, DOLLAR P, GIRSHICK R, et al. Feature pyramid networks for object detection [C] // IEEE Conference on Computer Vision and Pattern Recognition. Hawaii: IEEE, 2017: 936-944.
|
|
|
| [17] |
陈幻杰, 王琦琦, 杨国威, 等 多尺度卷积特征融合的SSD目标检测算法[J]. 计算机科学与探索, 2019, 13 (6): 1049- 1061
CHEN Huan-jie, WANG Qi-qi, YANG Guo-wei, et al SSD object detection algorithm with multi-scale convolution feature fusion[J]. Journal of Frontiers of Computer Science and Technology, 2019, 13 (6): 1049- 1061
|
|
|
| [18] |
ZEILER M D, FERGUS R. Visualizing and understanding convolutional networks [C] // European Conference on Computer Vision. Zurich: ECCV, 2014: 818-833.
|
|
|
| [19] |
王俊强, 李建胜, 周学文, 等 改进的SSD算法及其对遥感影像小目标检测性能的分析[J]. 光学学报, 2019, 39 (6): 373- 382
WANG Jun-qiang, LI Jians-heng, ZHOU Xue-wen, et al Improved SSD algorithm and its performance analysis of small target detection in remote sensing images[J]. Acta Optica Sinica, 2019, 39 (6): 373- 382
|
|
|
| [20] |
张焯林, 赵建伟, 曹飞龙 构建带空洞卷积的深度神经网络重建高分辨率图像[J]. 模式识别与人工智能, 2019, 32 (3): 259- 267
ZHANG Zhuo-lin, ZHAO Jian-wei, CAO Fei-long Building deep neural networks with dilated convolutions to reconstruct high-resolution image[J]. Pattern Recognition and Artificial Intelligence, 2019, 32 (3): 259- 267
|
|
|
| [21] |
LONG J, SHELHAMER E, DARRELL T Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 39 (4): 640- 651
|
|
|
| [22] |
HU J, SHEN L, SAMUEL A, et al. Squeeze-and-excitation networks [J]. arXiv Preprint arXiv: 1709.01507, 2017.
|
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
