复杂背景下机场道面细带状结构病害检测算法

李海丰; 韩红阳

doi:10.13700/j.bh.1001-5965.2020.0512

复杂背景下机场道面细带状结构病害检测算法

doi: 10.13700/j.bh.1001-5965.2020.0512

李海丰^,,
韩红阳

中国民航大学计算机科学与技术学院, 天津 300300

基金项目:

国家重点研发计划 2019YFB1310601

详细信息

通讯作者:
李海丰, E-mail: lihf_cauc@126.com

中图分类号: TP391
计量
- 文章访问数: 223
- HTML全文浏览量: 35
- PDF下载量: 45
- 被引次数: 0
出版历程
- 收稿日期: 2020-09-11
- 录用日期: 2020-11-13
- 网络出版日期: 2022-01-20

Algorithm to detect thin strip-shaped structural diseases on airport pavement in complex background

LI Haifeng^,,
HAN Hongyang

College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China

Funds:

National Key R & D Program of China 2019YFB1310601

More Information

Corresponding author: LI Haifeng, E-mail: lihf_cauc@126.com

摘要

摘要:
机场道面裂缝、角隅断裂、接缝破碎、修补等病害宽度狭小、长短不一、图像中像素占比少，呈细带状结构，且与复杂背景对比度低，现有检测算法效果不佳。针对以上问题，提出了一种基于注意力机制与特征融合的深度神经网络模型DetMSPNet。首先，利用注意力机制模块CBAM，使得特征学习更加专注于细带状结构病害区域，抑制干扰信息；其次，构建残差空洞金字塔模块，提取不同尺度空间下的特征信息；然后，设计最大池化支路，便于之后浅、深层不同层次特征进行融合，加强模型对于病害的定位能力，并且将深层特征输入3种不同扩张率的扩张卷积和金字塔池化模块，使得病害特征包含更多全局上下文信息；最后，对所有层输出的病害特征信息进行融合，实现不同尺度、不同层次特征的信息互补。与目前3种经典的目标检测算法在机场道面病害图像数据集APD上做了对比实验，结果表明：所提算法的mAP达到78.51%，优于对比算法。所提DetMSPNet模型，提高了算法对机场道面细带状结构病害检测中宽度狭小、长短不一、图像中像素占比少、与复杂背景对比度低等情况的适应能力。
- 机场道面细带状结构病害 /
- DetMSPNet /
- 注意力机制 /
- 特征融合 /
- 复杂背景
Abstract:
The structural diseases on airport pavement, such as cracks, corner fractures, broken seams, and repairs, have the characteristics of narrow width, different length, and less pixel proportion in the image, which show a thin strip-shaped structure, and the contrast is low in the complex background. These factors lead to detection failure when using the existing detection algorithms. To solve these problems, a deep neural network model, named as DetMSPNet, based on attention mechanism and feature fusion is proposed. First, the attention mechanism module CBAM is used to make the feature learning more focused on the disease area of the thin strip-shaped structure and suppress the interference information. Second, the residual atrous pyramid module is constructed to extract the feature information with different scales. Then, the maximum pooling branch is designed to facilitate the fusion of different features from shallow and deep layers, and improve the positioning ability of the model for diseases. In addition, the deep features are fed into three dilated convolutions with different dilated rates and pyramid pooling modules, so that the disease features contain more global context information. Finally, the disease features generated from all levels are fused to fulfil the information complementation from different scales and different levels. The comparative experiment was conducted with three classical object detection algorithms on APD dataset, and the results show that the proposed algorithm achieves an mAP of 78.51%, which is better than its counterparts. The proposed DetMSPNet improves the adaptability of the algorithm to the narrow width, different length, less proportion of pixels in the image and low contrast with complex background for the detection of thin strip-shaped structural diseases on airport pavement. The experimental results show that the average detection accuracy of the proposed algorithm is improved.
- thin strip-shaped structural diseases on airport pavement /
- DetMSPNet /
- attention mechanism /
- feature fusion /
- complex background

HTML全文

图 1 DetMSPNet详细结构

Figure 1. Detailed structure of DetMSPNet

下载: 全尺寸图片幻灯片

图 2 嵌入CBAM的DetBlock

Figure 2. DetBlock embedded in CBAM

下载: 全尺寸图片幻灯片

图 3 三个不同扩张率的扩张卷积

Figure 3. Dilated convolutions with three dilated rates

下载: 全尺寸图片幻灯片

图 4 机场道面安全检测机器人

Figure 4. Airport pavement safety detection robot

下载: 全尺寸图片幻灯片

图 5 不同算法在APD数据集上的可视化结果展示

Figure 5. Visualization results of various algorithms on APD dataset

下载: 全尺寸图片幻灯片

表 1 APD数据集详情

Table 1. Details of APD dataset

参数	训练	验证	测试
图像大小/像素	900×600	900×600	900×600
图像数量(扩充后)/张	7 213	1 803	547

下载: 导出CSV

表 2 机场道面APD数据集检测结果

Table 2. Detection results on airport pavement APD dataset

算法	网络	平均速度/s	AP/%				mAP/%
算法	网络	平均速度/s	裂缝	接缝破碎	角隅断裂	修补	mAP/%
STDN	Densenet169	0.023/(GPU)	28.96	59.42	89.37	79.47	64.28
Faster R-CNN	ResNet-101	0.062(GPU)	40.86	68.53	90.47	74.34	68.55
Cascade R-CNN	DetNet	0.137(GPU)	47.96	87.11	90.91	82.95	77.23
本文算法	DetMSPNet	0.167(GPU)	49.44	87.54	92.64	84.42	78.51

下载: 导出CSV

表 3 CBAM模块的有效性

Table 3. Effectiveness of CBAM module

网络	AP/%				mAP/%
网络	裂缝	接缝破碎	角隅断裂	修补	mAP/%
DetMSPNet (无CBAM)	49.82	89.09	90.91	83.67	78.37
DetMSPNet	49.44	87.54	92.64	84.42	78.51

下载: 导出CSV

表 4 DetASPP模块的有效性

Table 4. Effectiveness of DetASPP module

网络	AP/%				mAP/%
网络	裂缝	接缝破碎	角隅断裂	修补	mAP/%
DetMSPNet (无DetASPP)	48.55	88.35	92.81	83.19	78.23
DetMSPNet	49.44	87.54	92.64	84.42	78.51

下载: 导出CSV

表 5 最大池化支路的有效性

Table 5. Effectiveness of maximum pooling branch

网络	AP/%				mAP/%
网络	裂缝	接缝破碎	角隅断裂	修补	mAP/%
DetMSPNet(无最大池化支路)	49.18	87.81	90.91	83.19	77.77
DetMSPNet	49.44	87.54	92.64	84.42	78.51

下载: 导出CSV

表 6 三种不同扩张率的扩张卷积的有效性

Table 6. Effectiveness of dilated convolution with three dilated rates

网络	AP/%				mAP/%
网络	裂缝	接缝破碎	角隅断裂	修补	mAP/%
DetMSPNet(无扩张卷积)	48.75	88.31	92.01	83.94	78.25
DetMSPNet	49.44	87.54	92.64	84.42	78.51

下载: 导出CSV

表 7 金字塔池化模块的有效性

Table 7. Effectiveness of pyramid pooling module

网络	AP/%				mAP/%
网络	裂缝	接缝破碎	角隅断裂	修补	mAP/%
DetMSPNet(无PPM)	48.06	88.69	90.91	83.32	77.75
DetMSPNet	49.44	87.54	92.64	84.42	78.51

下载: 导出CSV

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