Lightweight object detection algorithm based on multi-directional feature pyramid

Chuang BAI; Ying-jie WANG; Yu YAN; Milena DJUKANOVIC

doi:10.37188/CJLCD.2021-0018

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Lightweight object detection algorithm based on multi-directional feature pyramid

Target Detectionand Tracking | 更新时间：2021-11-08

- Lightweight object detection algorithm based on multi-directional feature pyramid
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 11, Pages: 1516-1524(2021)
- 作者机构：
  
  1.长沙理工大学物理与电子科学学院, 湖南长沙 410114
  2.柔性电子材料基因工程湖南省重点实验室, 湖南长沙 410114
  3.黑山大学电气工程学院, 黑山共和国波德戈里察 81000
- 作者简介：
- 基金信息：
  
  China-Montenegro 3rd Science & Technology Exchange and Cooperation Project(3-7);Double First-class Scientific Research International Cooperation Expansion Project of Changsha University of Science and Technology(2019ic18);Open Research Fund of Hunan Provincial Key Laboratory of Flexible Electronic Materials Genome Engineering(202005)
- DOI：10.37188/CJLCD.2021-0018
  CLC： TP391.41
- Received：23 January 2021，
  
  Revised：28 March 2021，
  
  Published：2021-11
- 稿件说明：
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Chuang BAI, Ying-jie WANG, Yu YAN, et al. Lightweight object detection algorithm based on multi-directional feature pyramid[J]. Chinese journal of liquid crystals and displays, 2021, 36(11): 1516-1524.
DOI：

Chuang BAI, Ying-jie WANG, Yu YAN, et al. Lightweight object detection algorithm based on multi-directional feature pyramid[J]. Chinese journal of liquid crystals and displays, 2021, 36(11): 1516-1524. DOI： 10.37188/CJLCD.2021-0018.

摘要

针对实时目标检测算法Tiny YOLOv3存在的深层特征难以提取、实时性不能满足需求、边界框定位不够准确的问题，提出一种改进的轻量级检测模型MTYOLO (MdFPN Tiny YOLOv3)。该模型构造多向特征金字塔网络(MdFPN)代替简单级联，充分完成了多层语义信息的提取与融合；使用深度可分离卷积代替标准卷积，有效降低网络复杂度，提升了检测的实时性能；采用Complete IOU loss (CIOU loss)代替MSE作为回归损失函数，极大地提高了边界框的回归精度。在PASCAL VOC和COCO数据集上对MTYOLO进行测试，结果表明，改进后模型的mAP最高可达到83.7%，检测速度最快可达到205 fps。

Abstract

Aiming at the problems that the deep features are difficult to extract

the real-time performance cannot meet the requirements

and the bounding box positioning is not accurate enough of the real-time object detection algorithm Tiny YOLOv3

an improved lightweight detection model MTYOLO (MdFPN Tiny YOLOv3) is proposed. The model constructs multi-directional feature pyramid network (MdFPN) instead of simple concatenation to adequately complete the extraction and fusion of multi-layer semantic information. The deep separable convolution is used instead of standard convolution to effectively reduce the complexity of the network and improve the real-time performance of detection. The complete IOU loss (CIOU loss) is used instead of MSE as the regression loss function

which greatly improves the regression accuracy of bounding box. The results which MTYOLO is tested on PASCAL VOC and COCO datasets show that the mAP of the improved model can reach 78.7% and the detection speed can reach 205 fps.

关键词

Keywords

references

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Related Institution

Faculty of Electrical Engineering，University of Montenegro，Podgorica 81000，Montenegro

Hunan Provincial Key Laboratory of Flexible Electronic Materials Genome Engineering， Changsha 4100114

College of Physics and Electronic Science， Changsha University of Technology

School of Internet of Things Engineering， Wuxi University

School of Computer Science， Nanjing University of Information Science and Technology

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