并联提取与特征融合注意力网络下的裂缝检测

针对裂缝检测现有方法精度低、噪声多和细节丢失等问题,设计了一种基于并行提取和注意力融合网络的裂缝检测算法。首先,利用不同深度的多尺度卷积并联神经网络提取裂缝场景的高、低级特征;然后,为了提高检测精度,针对裂缝场景的特征,结合像素注意力机制对裂缝场景的高、低级特征进行有效融合,得到用于裂缝检测的有效融合特征;最后,利用非线性映射进行裂缝检测结果输出。实验结果表明,所提算法能够获得高精度检测结果的有效特征,裂缝检测结果细节更加清晰,且有监督学习方式在很大程度上消除了检测结果的噪声干扰,得到了视觉效果更佳的检测结果;所提算法在精确率和召回率等定量指标评价中同样具有良好的表现,裂缝检测精确率达到85%。

Crack detection under parallel extraction and feature fusion attention network

A crack detection algorithm based on parallel extraction and attention fusion network is designed to address the problems of low accuracy, much noise and detail loss of existing methods for crack detection. First, the high and low-level features of the crack scene are extracted using multi-scale convolutional parallel neural networks with different depths; then, to improve the detection accuracy, the high and low-level features of the crack scene are effectively fused with the pixel attention mechanism for the features of the crack scene to obtain effective fusion features for crack detection; finally, the crack detection results are output using nonlinear mapping. The experimental results show that the proposed algorithm can obtain effective features for high-precision detection results, the details of crack detection results are clearer, and the supervised learning approach largely eliminates the noise interference of detection results and obtains detection results with better visual effects; the proposed algorithm also has good performance in the evaluation of quantitative indexes such as accuracy rate and recall rate, and the accuracy rate of crack detection reaches 85%.