Improved SSD Algorithm and Its Application in Subway Security Detection

doi:10.19678/j.issn.1000-3428.0058283

Abstract

Abstract: In the detection of small objects,the traditional SSD algorithm tends to miss the targets,and its detection accuracy is reduced.To address the problem,an improved algorithm is proposed.Each scale feature in the SSD algorithm is convoluted with the size being unchanged,and the corresponding features before and after the convolution are fused by using a lightweight network to generate a new set of pyramid feature layers.Then a detection unit based on the residual module is added to avoid increasing the network model capacity and computational complexity,and to enhance the detection ability of small targets.The experimental results on the PASCAL-VOC2007 small target data set show that,compared with traditional SSD,YOLOv3,Faster RCNN algorithms etc.,the mAP of the proposed algorithm is 8.5% higher than that of the traditional SSD,3.9% higher than that of the Faster RCNN algorithm,and 2% higher than that of the YOLOv3 algorithm.The proposed algorithm increases the FPS to 83 frames/s,and the mAP of the subway security image check to 77.8%.

Key words: SSD algorithm, network convergence, pyramid feature layer, residual module, detection unit, object detection

摘要： 针对传统SSD算法在检测小目标时容易漏检且检测精度不高的问题，提出一种改进算法。对SSD算法中各尺度特征进行尺寸大小不变的卷积操作，将卷积前后对应的特征进行轻量级网络融合，从而生成新的金字塔特征层，并加入基于残差模块的检测单元避免增加网络模型容量和运算复杂度，同时增强对小尺度目标的检测能力。基于PASCAL-VOC2007小目标数据集的实验结果表明，与传统SSD、YOLOv3、Faster RCNN等算法相比，在PASCAL-VOC2007小目标数据集中，该算法的mAP指标较传统SSD算法提高8.5%，较Faster RCNN算法提高3.9%，较YOLOv3提高2%，FPS达到83 frame/s，其检测地铁安检图片的mAP达到77.8%。

关键词: SSD算法, 网络融合, 金字塔特征层, 残差模块, 检测单元, 目标检测

CLC Number:

TP391

ZHANG Zhen, LI Mengzhou, LI Haofang, MA Junqiang. Improved SSD Algorithm and Its Application in Subway Security Detection[J]. Computer Engineering, 2021, 47(7): 314-320.

张震, 李孟洲, 李浩方, 马军强. 改进SSD算法及其在地铁安检中的应用[J]. 计算机工程, 2021, 47(7): 314-320.

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References

[1] 吕苗苗,孙建明.基于改进高斯混合模型的运动图像目标检测算法[J].半导体光电,2019,40(6):874-878,885. LÜ M M,SUN J M.Moving image target detection based on modified Gaussian mixture model[J].Semiconductor Optoelectronics,2019,40(6):874-878,885.(in Chinese)
[2] 曲欣欣.机场安检人员的可靠性研究[D].沈阳:沈阳航空工业学院,2009. QU X X.Research on the reliability of airport security inspectors[D].Shenyang:Shenyang Institute of Aeronautical Technology,2009.(in Chinese)
[3] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2014:580-587.
[4] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[C]//Proceedings of 2016 IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2016:779-788.
[5] REDMON J,FARHADI A.YOLO9000:better,faster,stronger[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:6517-6525.
[6] GIRSHICK R.Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:1440-1448.
[7] REN S Q,HE K M,GIRSHICK R,et al.Faster R-CNN:towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems.Cambridge,USA:MIT Press,2015:91-99.
[8] LIU W,ANGUELOV D,ERHAN D,et al.SSD:single shot multibox detector[C]//Proceedings of European Conference on Computer Vision.Aire-la-Ville,Switzerland:Eurographics Association Press,2016:21-37.
[9] FU C Y,LIU W,RANGA A,et al.DSSD:deconvolutional single shot detector[EB/OL].(2017-01-23)[2020-07-10].http://cn.arxiv.org/abs/1701.06659.
[10] 李晖晖,周康鹏,韩太初.基于CReLU和FPN改进的SSD舰船目标检测[J].仪器仪表学报,2020,41(4):183-190. LI H H,ZHOU K P,HAN T C.Improved SSD ship target detection based on CReLU and FPN[J].Journal of Instrumentation,2020,41(4):183-190. (in Chinese)
[11] 李善军,胡定一,高淑敏,等.基于改进SSD的柑橘实时分类检测[J].农业工程学报,2019,35(24):307-313. LI S J,HU D Y,GAO S M,et al.Real time classification and detection of Citrus Based on improved SSD[J].Journal of Agricultural Engineering,2019,35(24):307-313.(in Chinese)
[12] 刘慧,张礼帅,沈跃,等.基于改进SSD的果园行人实时检测方法[J].农业机械学报,2019,50(4):29-35,101. LIU H,ZHANG L S,SHEN Y,et al.Real time pedestrian detection in orchard based on improved SSD[J].Journal of Agricultural Machinery,2019,50(4):29-35,101.(in Chinese)
[13] CHEN C Y,LIU M Y,TUZEL O,et al.R-CNN for small object detection[C]//Proceedings of Asian Conference on Computer Vision.Berlin,Germany:Springer,2016:214-230.
[14] BELL S,LAWRENCE Z C,BALA K,et al.Inside-outside net:detecting objects in context with skip pooling and recurrent neural networks[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:2874-2883.
[15] KONG T,YAO A B,CHEN Y R,et al.HyperNet:towards accurate region proposal generation and joint object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:845-853.
[16] CAO G M,XIE X M,YANG W Z,et al.Feature-fused SSD:fast detection for small objects[EB/OL].(2018-11-27)[2020-07-10].https://arxiv.org/ftp/arxiv/papers/1709/1709.05054.pdf.
[17] LI Z X,ZHOU F Q.FSSD:feature fusion single shot multibox detector[EB/OL].(2018-05-17)[2020-07-10].https://arxiv.org/abs/1712.00960.
[18] JEONG J,PARK H,KWAK N.Enhancement of SSD by concatenating feature maps for object detection[EB/OL].(2017-05-26)[2020-07-10].https://arxiv.org/abs/1705.09587.
[19] 谭红臣,李淑华,刘彬,等.特征增强的SSD算法及其在目标检测中的应用[J].计算机辅助设计与图形学学报,2019,31(4):573-579. TAN H C,LI S H,LIU B,et al.Feature enhanced SSD algorithm and its application in target detection[J].Journal of Computer Aided Design and Graphics,2019,31(4):573-579.(in Chinese)
[20] LIU W,RABINOVICH A,BERG A C.ParseNet:looking wider to see better[EB/OL].(2015-11-19)[2020-07-10].https://arxiv.org/pdf/1506.04579.pdf.
[21] 孙皓泽,常天庆,张雷,等.基于轻量级网络的装甲目标快速检测[J].计算机辅助设计与图形学学报,2019,31(7):1010-1121. SUN H Z,CHANG T Q,ZHANG L,et al.Fast detection of armored target based on lightweight network[J].Journal of Computer Aided Design and Graphics,2019,31(7):1010-1121.(in Chinese)

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