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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2020

14.03.2020

A Balanced Feature Fusion SSD for Object Detection

verfasst von: Hui Zhao, Zhiwei Li, Lufa Fang, Tianqi Zhang

Erschienen in: Neural Processing Letters | Ausgabe 3/2020

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Abstract

Single shot multibox detector (SSD) takes several feature layers for object detection, but each layer is used independently. This structure may ignore some context information and is not conducive to improving the detection accuracy of small objects. Moreover, the imbalances of samples and multi-tasks during SSD training process can lead to inefficient training and model degradation. In order to improve the performance of SSD, this paper proposes a balanced feature fusion SSD (BFSSD) algorithm. Firstly, a feature fusion module is proposed to fuse and refine different layers of the feature pyramid. Then, a more balanced L1 loss function is proposed to further solve these imbalances. Finally, our model is trained with Pascal VOC2007 and VOC2012 trainval datasets and tested on Pascal VOC2007 test datasets. Simulation results show that, for the input size of 300 × 300, BFSSD exceeds the best results provided by the conventional SSD and other advanced object detection algorithms.
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Fußnoten
Literatur
1.
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) SSD: single shot multibox detector. In: European conference on computer vision. Springer, Cham, pp 21–37
2.
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:​1409.​1556
3.
Chen C, Liu MY, Tuzel O, Xiao J (2016) R-CNN for small object detection. In: Asian conference on computer vision. Springer, Cham, pp 214–230
4.
5.
Dai J, Li Y, He K, Sun J (2016) RFCN: object detection via region based fully convolutional networks. In: Neural Information Processing Systems, pp 379–387
6.
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision. Springer, Cham, pp 818–833
7.
Lin TY, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2117–2125
8.
Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8759–8768
9.
Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24(12):5659–5670MathSciNetCrossRef
10.
11.
Zhang J, Yu J, Tao D (2018) Local deep-feature alignment for unsupervised dimension reduction. IEEE Trans Image Process 27(5):2420–2432MathSciNetCrossRef
12.
Yu J, Zhu C, Zhang J, Huang Q, Tao D (2019) Spatial pyramid-enhanced NetVLAD with weighted triplet loss for place recognition. IEEE Trans Neural Netw Learn Syst 31(2):661–674CrossRef
13.
Kendall A, Gal Y, Cipolla R (2018) Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7482–7491
14.
Pang J, Chen K, Shi J, Feng H, Ouyang W, Lin D (2019) Libra r-cnn: towards balanced learning for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 821–830
15.
Hong C, Yu J, Zhang J, Jin X, Lee KH (2018) Multi-modal face pose estimation with multi-task manifold deep learning. IEEE Trans Ind Inform 15(7):3952–3961CrossRef
16.
Yu J, Zhang B, Kuang Z, Lin D, Fan J (2016) iPrivacy: image privacy protection by identifying sensitive objects via deep multi-task learning. IEEE Trans Inf Forensics Secur 12(5):1005–1016CrossRef
17.
Yu J, Tao D, Wang M, Rui Y (2014) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern 45(4):767–779CrossRef
18.
Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 580–587
19.
Girshick R (2015) Fast R-CNN. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448
20.
Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149CrossRef
21.
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 779–788
22.
Krizhevsky A, Sutskever I, Hinton G (2012) ImageNet classification with deep convolutional neural networks. In: Neural Information Processing Systems, pp 1097–1105
23.
Uijlings JR, Van De Sande KE, Gevers T, Smeulders AW (2013) Selective search for object recognition. Int J Comput Vis 104(2):154–171CrossRef
24.
He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916CrossRef
25.
Cai Z, Vasconcelos N (2018) Cascade r-cnn: delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6154–6162
26.
He K, Gkioxari G, Dollár P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 2961–2969
27.
Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7263–7271
28.
Cai Z, Fan Q, Feris RS, Vasconcelos N (2016) A unified multi-scale deep convolutional neural network for fast object detection. In: European conference on computer vision. Springer, Cham, pp 354–370
29.
Jeong J, Park H, Kwak N (2017) Enhancement of SSD by concatenating feature maps for object detection. arXiv preprint arXiv:​1705.​09587
30.
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
31.
Shen Z, Liu Z, Li J, Jiang YG, Chen Y, Xue X (2017) Dsod: learning deeply supervised object detectors from scratch. In: Proceedings of the IEEE international conference on computer vision, pp 1919–1927
32.
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708
33.
Choi J, Lee K, Jeong J et al (2019) Two-layer residual feature fusion for object detection. In: International conference on pattern recognition applications and methods, pp 352–359
34.
Shrivastava A, Gupta A, Girshick R (2016) Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 761–769
35.
Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988
36.
Yu J, Jiang Y, Wang Z, Cao Z, Huang T (2016) Unitbox: an advanced object detection network. In: Proceedings of the 24th ACM international conference on multimedia. ACM, pp 516–520
37.
Jiang B, Luo R, Mao J, Xiao T, Jiang Y (2018) Acquisition of localization confidence for accurate object detection. In: Proceedings of the European conference on computer vision (ECCV), pp 784–799
38.
Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (VOC) challenge. Int J Comput Vis 88(2):303–338CrossRef
Metadaten
Titel
A Balanced Feature Fusion SSD for Object Detection
verfasst von
Hui Zhao
Zhiwei Li
Lufa Fang
Tianqi Zhang
Publikationsdatum
14.03.2020
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10228-5

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