Small Object Detection Algorithm Based on Multiscale Receptive Field Fusion

doi:10.3778/j.issn.1002-8331.2101-0009

Abstract

Abstract: Aiming at the problem of low detection accuracy of general object detection algorithm in small target detection, a small object detection algorithm S-RetinaNet based on multi-scale receptive field fusion is proposed. The algorithm uses residual neural network （ResNet） to extract image features, uses recursive feature pyramid network（RFPN） to fuse features, and processes three outputs of RFPN by multiscale receptive field fusion（MRFF） to improve the ability of small target detection. Experimental results show that, compared with the original RetinaNet algorithm, the mean Average Precision（mAP） of S-RetinaNet algorithm on PASCAL VOC dataset and the average precision（AP） of MS COCO dataset are improved by 2.3 and 1.6 percentage points respectively, and the average precision small（APS） of small object detection accuracy is improved more significantly, increased by 2.7 percentage points.

Key words: neural network, small object detection, receptive field, feature pyramid

摘要： 针对通用目标检测算法在检测小目标时检测精度低的问题，提出一种基于多尺度感受野融合的小目标检测算法S-RetinaNet。该算法采用残差神经网络（residual neural network，ResNet）提取出图像的特征，利用递归特征金字塔网络（recursive feature pyramid network，RFPN）对特征进行融合，通过多尺度感受野融合模块（multiscale receptive field fusion，MRFF）分别处理RFPN的三个输出，提升对小目标的检测能力。实验表明，相比改进前的RetinaNet算法，S-RetinaNet算法在PASCAL VOC数据集上的均值平均精度（mean average precision，mAP）和MS COCO数据集上的平均精度（average precision，AP）分别提高了2.3和1.6个百分点，其中小目标检测精度（average precision small，APS）更为显著，提升了2.7个百分点。

关键词: 神经网络, 小目标检测, 感受野, 特征金字塔

LI Chenghao, ZHANG Jing, HU Li, XIAO Xianpeng, ZHANG Hua. Small Object Detection Algorithm Based on Multiscale Receptive Field Fusion[J]. Computer Engineering and Applications, 2022, 58(12): 177-182.

李成豪, 张静, 胡莉, 肖贤鹏, 张华. 基于多尺度感受野融合的小目标检测算法[J]. 计算机工程与应用, 2022, 58(12): 177-182.

References

[1] LECUN Y，BOSER B，DENKER J S，et al.Backpropagation applied to handwritten zip code recognition[J].Neural Computation，1989，1（4）：541-551.
[2] KRIZHEVSKY A，SUTSKEVER I，HINTON G E.ImageNet classification with deep convolutional neural networks[C]//Proceedings of 2012 Neural Information Processing Systems（NIPS），Lake Tahoe，NV，Dec 3-6，2012.Cambridge，MA：MIT Press，2012：1097-1105.
[3] 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（CVPR），Columbus，OH，Jun 24-27，2014.Piscataway，NJ：IEEE，2014：580-587.
[4] GIRSHICK R.Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision（ICCV），Santiago，Chile，Dec 7-13，2015.Piscataway，NJ：IEEE，2015：1440-1448.
[5] REN S，HE K，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，2016，39（6）：1137-1149.
[6] REDMON J，DIVVALA S，GIRSHICK R，et al.You only look once：unified，real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition（CVPR），Las Vegas，NV，Jun 27-30，2016.Piscataway，NJ：IEEE，2016：779-788.
[7] LIU W，ANGUELOV D，ERHAN D，et al.SSD：single shot multibox detector[C]//European Conference on Computer Vision（ECCV），Amsterdam，Holland，Oct 8-16，2016.Berlin：Springer，2016：21-37.
[8] LIN T Y，GOYAL P，GIRSHICK R，et al.Focal loss for dense object detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence，2020，42（2）：318-327.
[9] 李明熹，林正奎，曲毅.计算机视觉下的车辆目标检测算法综述[J].计算机工程与应用，2019，55（24）：20-28.
LI M X，LIN Z K，QU Y.Survey of vehicle object detection algorithm in computer vision[J].Computer Engineering and Application，2019，55（24）：20-28.
[10] FU C Y，LIU W，RANG A A，et al.DSSD：deconvolutional single shot detector[J].arXiv：1701.06659，2017.
[11] 王文光，李强，林茂松，等.基于改进SSD的高效目标检测方法[J].计算机工程与应用，2019，55（13）：28-35.
WANG W G，LI Q，LIN M S，et al.Efficient object detection method based on improved SSD[J].Computer Engineering and Applications，2019，55（13）：28-35.
[12] LIN T Y，DOLLáR P，GIRSHICK R，et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition（CVPR），Honolulu，HI，Jul 21-26，2017.Piscataway，NJ：IEEE，2017：2117-2125.
[13] QIAO S，CHEN L C，YUILLE A.DetectoRS：detecting objects with recursive feature pyramid and switchable atrous convolution[J].arXiv：2006.02334，2020.
[14] HU P，RAMANAN D.Finding tiny faces[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition（CVPR），Honolulu，HI，Jul 21-26，2017.Piscataway，NJ：IEEE，2017：951-959.
[15] SZEGEDY C，LIU W，JIA Y，et al.Going deeper with convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition（CVPR），Boston，MA，Jun 8-12，2015.Piscataway，NJ：IEEE，2015：1-9.
[16] HOWARD A G，ZHU M，CHEN B，et al.MobileNets：efficient convolutional neural networks for mobile vision applications[J].arXiv：1704.04861，2017.