基于边缘感知的深度神经网络红外装甲目标检测

盛大俊; 张强

基于边缘感知的深度神经网络红外装甲目标检测

盛大俊¹,
张强^2, ,

1.
信阳学院大数据与人工智能学院，河南信阳 464000
2.
中国人民解放军空军第一航空学院，河南信阳 464000

基金项目:

装备预研基金资助课题

详细信息

作者简介:
盛大俊（1981-），男，讲师，研究方向：制导测试技术，计算机视觉应用

通讯作者:
张强（1975-），男，高工，研究方向：光电系统维修与测试，红外制导技术等，E-mail：x0376y@163.com

中图分类号: TP753
计量
- 文章访问数: 323
- HTML全文浏览量: 122
- PDF下载量: 58
出版历程
- 收稿日期: 2020-05-09
- 修回日期: 2020-09-02
- 刊出日期: 2021-08-19

Infrared Armored Target Detection Based on Edge-perception in Deep Neural Network

SHENG Dajun¹,
ZHANG Qiang^2, ,

1.
College of Big Data and Artificial Intelligence, Xinyang University, Xinyang 464000, China
2.
The first Aviation Academy, Chinese Air force, Xinyang 464000, China

摘要

摘要: 装甲目标自动检测一直是红外制导领域的研究热点与难点。解决该问题的传统方法是提取目标的低层次特征，并对特征分类器进行训练。然而，由于传统的检测算法不能覆盖所有的目标模式，在实际应用中的检测性能受到限制。本文受边缘感知模型的启发，提出了一种基于边缘感知的改进深度网络，该网络是通过边缘感知融合模块提升装甲轮廓精度，利用特征提取模块和上下文聚合模块的优势，能够更好地适应目标的形态变化，具有较高的检测与识别的精度。验证结果表明，本文提出的装甲检测网络模型可以有效地提高红外图像中装甲的检测与定位精度。
- 装甲车辆 /
- 红外图像 /
- 目标检测 /
- 边缘感知 /
- 自寻的导弹 /
- 金字塔池化
Abstract: Automatic detection of armored targets has always been the most challenging problem in the field of infrared guidance. Traditional models address this problem by extracting the low-level features of an object and then training the feature classifier. However, because traditional detection algorithms can not cover all object patterns, the detection performance in practical applications is limited. Inspired by the edge-aware model, this study proposes an improved deep network based on edge perception. The network improves the accuracy of the armored contour through an edge-aware fusion module. By exploiting he advantages of the feature extraction module and context aggregation module, it can better adapt to the shape changes of objects and has high detection and recognition accuracy. The results show that the proposed armored detection network model can effectively improve the accuracy of detection and positioning in infrared images.
- armored vehicle /
- infrared image /
- target detection /
- edge perception /
- homing missile /
- pyramid pooling

HTML全文

图 1 基于边缘感知的目标检测模型框架

Figure 1. Model frameworks of object detection based on edge-aware

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图 2 上下文聚合模块

Figure 2. Context aggregation module

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图 3 边缘感知融合模块

Figure 3. Edge-aware fusion module

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图 4 检测率与FPPI的关系曲线

Figure 4. Relationship-curves between detection rate and FPPI

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图 5 不同对比算法的性能对比

Figure 5. Performance comparison for different detection algorithms

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图 6 不同对比算法对不同红外图像的装甲检测的结果，其中(a)-(f)分别代表不同图像

Figure 6. Results of different comparison algorithms for different images, where (a)-(f) respectively represent the different images

下载: 全尺寸图片幻灯片

表 1 装甲检测结果统计

Table 1 Statistics of detection results

Classification	Result
Positive	TP（True Positive）	FP（False Positive）
Negative	FN（False Negative）	TN（True Negative）

下载: 导出CSV

表 2 不同测试子集的特点

Table 2 Characteristics of different test subsets

Subset	Characteristic
A	The target is fuzzy, the contrast is low, and some turrets are covered.
B	The armored target is small, and there are many bright areas in the background.
C	The armor area is large and full of the whole infrared image.
D	The armor boundary is not obvious and the background is super saturation.
E	The noise is very large and the internal details of the target are uneven.
F	The armored target is fuzzy and partially occlused, and some caterpillar bands are coved by weeds.

下载: 导出CSV

表 3 不同测试数据集下检测精度

Table 3 Detection Rates under Different Testing Data Sets

Datasets	Models
Datasets	SDD	DenseNet	ResNet	ConvNet	YOLO-v2	Proposed
A	73.32%	84.34%	86.61%	85.90%	89.15%	90.06%
B	62.41%	65.43%	72.15%	77.00%	77.59%	77.14%
C	69.12%	73.22%	74.35%	72.24%	77.09%	78.07%
D	78.28%	87.08%	89.40%	86.01%	88.36%	89.44%
E	51.91%	52.90%	59.75%	59.11%	58.69%	59.58%
F	48.65%	51.55%	60.11%	52.17%	60.20	60.21%

下载: 导出CSV

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