Lightweight SSD object detection method based on feature fusion

Tian-cheng WU; Xiao-quan WANG; Yi-jun CAI; You-bo JING; Cheng-ying CHEN

doi:10.37188/CJLCD.2021-0007

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Lightweight SSD object detection method based on feature fusion

Image Processing | 更新时间：2021-09-30

- Lightweight SSD object detection method based on feature fusion
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 10, Pages: 1437-1444(2021)
- 作者机构：
  
  1.厦门理工学院光电与通信工程学院, 福建厦门 361024
  2.中国科学院微电子研究所, 北京 100029
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(61704143);National Natural Science Foundation of China(62005232);Natural Science Foundation of Fujian Province(2018J01566);Xiamen Youth Innovation Fund(3502Z20206074)
- DOI：10.37188/CJLCD.2021-0007
  CLC： TP91.4
- Received：07 January 2021，
  
  Revised：15 March 2021，
  
  Published：2021-10
- 稿件说明：
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Tian-cheng WU, Xiao-quan WANG, Yi-jun CAI, et al. Lightweight SSD object detection method based on feature fusion[J]. Chinese journal of liquid crystals and displays, 2021, 36(10): 1437-1444.
DOI：

Tian-cheng WU, Xiao-quan WANG, Yi-jun CAI, et al. Lightweight SSD object detection method based on feature fusion[J]. Chinese journal of liquid crystals and displays, 2021, 36(10): 1437-1444. DOI： 10.37188/CJLCD.2021-0007.

摘要

针对应用于嵌入式设备的轻量级目标检测算法，本文提出了一种基于特征融合的改进方案。针对目前MobileNetV2-SSD轻量级目标检测算法检测精度低、小目标检测效果差的问题，结合FPN特征金字塔的特征融合思想，选用了MobileNetV2-SSD中包含较多语义信息的3层特征层进行融合，利用多尺度检测的原理，将融合后的特征层重新生成特征金字塔进行目标检测。使用PASCAL VOC 2007数据集对改进后的目标检测模型和原模型进行对比测试，其检测精度mAP相对原模型提高了3.9%，达到了76.5%，同时模型对于小目标的检测效果也有了显著提升。最后将模型部署至嵌入式设备Jetson AGX Xavier中进行测试，改进后的模型在网络结构更加复杂的情况下检测速度接近原模型，达到了22 FPS的检测速度，可以实现嵌入式设备上的实时目标检测。

Abstract

An improved scheme based on feature fusion for the lightweight object detection algorithm deployed in embedded devices is proposed in this paper. The algorithm is based on the MobileNetV2-SSD lightweight object detection algorithm with the idea of FPN feature fusion. It combines the three feature layers of MobileNetV2-SSD that contain more semantic information

and is regenerated based on the fused feature layer Feature pyramid for object detection. The improved model and the original model are compared and tested on the PASCAL VOC data set. Compared with the original model

the mAP is increased by 3.6%

and reaches 76.5%. At the same time

the detection effect of small targets is significantly improved. Finally

it is tested in the embedded device Jetson AGX Xavier. The improved model has a detection speed close to the original model when the network structure is more complex

reaching the detection speed of 22FPS

which can realize real-time object detection on embedded devices.

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references

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