Xing Jian, Liu Ying, Zhang Guangzhu
College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China.
Sensors (Basel). 2024 Apr 26;24(9):2759. doi: 10.3390/s24092759.
Cracks provide the earliest and most immediate visual response to structural deterioration of asphalt pavements. Most of the current methods for crack detection are based on visible light sensors and convolutional neural networks. However, such an approach obviously limits the detection to daytime and good lighting conditions. Therefore, this paper proposes a crack detection technique cross-modal feature alignment of YOLOV5 based on visible and infrared images. The infrared spectrum characteristics of silicate concrete can be an important supplement. The adaptive illumination-aware weight generation module is introduced to compute illumination probability to guide the training of the fusion network. In order to alleviate the problem of weak alignment of the multi-scale feature map, the FA-BIFPN feature pyramid module is proposed. The parallel structure of a dual backbone network takes 40% less time to train than a single backbone network. As determined through validation on FLIR, LLVIP, and VEDAI bimodal datasets, the fused images have more stable performance compared to the visible images. In addition, the detector proposed in this paper surpasses the current advanced YOLOV5 unimodal detector and CFT cross-modal fusion module. In the publicly available bimodal road crack dataset, our method is able to detect cracks of 5 pixels with 98.3% accuracy under weak illumination.
裂缝是沥青路面结构劣化最早且最直接的视觉表现。当前大多数裂缝检测方法基于可见光传感器和卷积神经网络。然而，这种方法显然将检测限制在白天和良好光照条件下。因此，本文提出一种基于可见光和红外图像的YOLOV5跨模态特征对齐裂缝检测技术。硅酸盐混凝土的红外光谱特性可作为重要补充。引入自适应光照感知权重生成模块来计算光照概率，以指导融合网络的训练。为缓解多尺度特征图对齐较弱的问题，提出了FA-BIFPN特征金字塔模块。双主干网络的并行结构比单主干网络训练时间少40%。通过在FLIR、LLVIP和VEDAI双峰数据集上的验证表明，与可见光图像相比，融合图像具有更稳定的性能。此外，本文提出的检测器超越了当前先进的YOLOV5单模态检测器和CFT跨模态融合模块。在公开可用的双峰道路裂缝数据集中，我们的方法在弱光照下能够以98.3%的准确率检测出5像素的裂缝。
