Rapider-YOLOX: lightweight object detection network with high precision

Zhouyu GU; Yuecheng YU; Tiantian Zhe

doi:10.11959/j.issn.2096-6652.202303

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Rapider-YOLOX: lightweight object detection network with high precision

Papers and Reports | 更新时间：2024-06-05

- Rapider-YOLOX: lightweight object detection network with high precision
- Chinese Journal of Intelligent Science and Technology Vol. 5, Issue 1, Pages: 92-103(2023)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202303
  CLC： TP391
- Online First：2023-03，
  
  Published：15 March 2023
- 稿件说明：
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Zhouyu GU, Yuecheng YU, Tiantian Zhe. Rapider-YOLOX: lightweight object detection network with high precision[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(1): 92-103.
DOI：

Zhouyu GU, Yuecheng YU, Tiantian Zhe. Rapider-YOLOX: lightweight object detection network with high precision[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(1): 92-103. DOI： 10.11959/j.issn.2096-6652.202303.

摘要

作为一种轻量级网络结构，YOLOX-Nano具有运行速度快的优势。然而，该网络在实际应用中仍然存在特征提取能力较弱、检测精度不足的缺陷。为此，提出了一种综合平衡检测速度和检测精度的高效目标检测网络Rapider-YOLOX。首先，设计高效瓶颈模块，以提升原始 YOLOX-Nano 模型中深度卷积模块的特征提取能力。其次，设计软空间金字塔池化模块，以避免原始SPP模块容易出现丢失部分重要信息的现象，进一步提升多尺度信息融合及通道间信息交流的能力。最后，引入CIoU损失，利用预测框与真实框的中心距离及宽高比提升预测框的位置精度。在PASCAL VOC2007数据集上的实验结果表明，提出的Rapider-YOLOX模型的mAP达到77.92%，比原始YOLOX-Nano高3.79%。此外，在CUDA核心数仅为384的GT1030上，FPS达到45.40，在CPU上FPS也可达到23.94，从而在确保网络轻量级特性的同时，进一步提升了网络的检测精度和泛化性能。

Abstract

As a lightweight network structure

YOLOX-Nano has the advantage of fast running speed.However

the model still has the defects of weak feature extraction ability and insufficient detection accuracy in practical application.Therefore

an efficient object detection network Rapider-YOLOX which comprehensively balanced the detection speed and detection accuracy was proposed.Firstly

the highly efficient bottleneck module was designed to improve the feature extraction capability of depthwise convolutional blocks in the original YOLOX-Nano model.Secondly

the soft-SPP module was designed to avoid the loss of some important information in the original SPP module and improve the ability of multi-scale information fusion and information exchange between channels further.Finally

CIoU was introduced to improve the position accuracy of the prediction box by using the center distance and aspect ratio between the prediction box and the real box.The experimental results on PASCAL VOC2007 dataset showed that the mAP of Rapider-YOLOX model reached 77.92%

which was 3.79% higher than the original YOLOX-Nano.In addition

on GT1030 with only 384 CUDA cores

the FPS of the proposed method could reach 45.40.The FPS could also reach 23.94 on the CPU

which further improved detection accuracy and generalization performance of the network while ensuring the lightweight characteristics of the network.

关键词

Keywords

references

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