Aircraft target detection method based on improved SSD

LI Jing; YU Jia-cheng; ZHANG Ling-ling

doi:10.37188/CJLCD.2022-0183

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Aircraft target detection method based on improved SSD

Image Processing | 更新时间：2023-03-15

- Aircraft target detection method based on improved SSD
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 1, Pages: 128-137(2023)
- 作者机构：
  
  1.西安工业大学电子信息工程学院，陕西西安 710021
  2.西安工业大学兵器科学与技术学院，陕西西安 710021
- 作者简介：
- 基金信息：
  
  Dynamic Test and Intelligent Information Processing Innovation Team of Xi'an University of Technology(401-302021605)
- DOI：10.37188/CJLCD.2022-0183
  CLC：
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LI Jing, YU Jia-cheng, ZHANG Ling-ling. Aircraft target detection method based on improved SSD. [J]. Chinese Journal of Liquid Crystals and Displays 38(1):128-137(2023)
DOI：

LI Jing, YU Jia-cheng, ZHANG Ling-ling. Aircraft target detection method based on improved SSD. [J]. Chinese Journal of Liquid Crystals and Displays 38(1):128-137(2023) DOI： 10.37188/CJLCD.2022-0183.

摘要

针对航拍图像中对于小尺度的飞机目标出现漏检、错检的问题，在SSD（Single Shot MultiBox Detector）模型的基础上提出了一种改进SSD的航拍图像目标检测模型。首先，针对SSD模型中浅层特征图中缺乏语义、细节信息的问题，设计了一种特征融合机制，通过添加细节信息补充特征层和添加由递归反向路径获得的语义信息补充特征层来丰富浅层特征图的语义、细节信息。然后，针对SSD模型对通道以及空间信息的关注能力不足的问题，引入了结合通道和空间的混合注意力模块来提高模型整体的关注能力。最后，针对SSD模型中先验框与小尺度目标不匹配的问题，对先验框的比例进行了调整。使用自制的航拍图像数据集进行验证，结果表明改进后的模型检测精度为95.7%，相较于原模型提高了7.5%，检测速度达到30.8 FPS。

Abstract

For the problem of omission and misdetection of small-scale aircraft targets in aerial images， an improving SSD object detection model is proposed based on SSD （Single Shot MultiBox Detector） model. Firstly， in view of the lack of semantic and detailed information in the shallow feature map in the SSD model， a feature fusion mechanism is designed to enrich the semantic and detailed information of the shallow feature layer by adding the supplementary feature layer obtained from the recursive reverse path. Then， to address the problem of the SSD model to focus on the channels and spatial information， a hybrid attention module combining channels and space is introduced to improve the overall attention ability of the model. Finally， the proportion of prior boxes is adjusted for the problem of mismatch to small-scale targets in the SSD model. The self-made aerial images data set is used for verification. The results show that the improved algorithm accuracy is 95.7%， which is 7.5% higher than original SSD algorithm， and the detection speed is 30.8 FPS.

关键词

目标检测特征融合注意力机制航拍图像

Keywords

object detectionfeature fusionattentional mechanismaerial images

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