基于改进YOLOv5的变电站屋面工程缺陷检测算法研究

Research on detection algorithm for substation roof engineeringdefects using an improved YOLOv5 model

针对变电站建筑物屋面工程缺陷检测效率较低及检测精确度较差的问题,提出一种基于改进YOLOv5(you only look once version 5)的变电站屋面工程缺陷检测算法。首先,对图像进行预处理,减轻外界噪声给检测效果带来的影响。其次,在网络骨干中引入改进自注意力机制,提高计算效率,用多头自注意力层替换YOLOv5网络骨干末端的卷积层,使网络能够更好地捕捉全局关联信息。最后,在检测部分增加跨层加权级联结构,将浅层中缺陷的边缘信息、轮廓信息融入到深层特征中,提高网络对缺陷边界回归的精确度。实验结果表明,本文提出的改进YOLOv5变电站屋面工程缺陷检测算法对保温层、隔离层、隔汽层、防水层和找平层这5类工序的缺陷检测的平均精度均值达到了93.2%,每秒帧数达到163.5帧/s,解决了实际工程环境中出现的变电站屋面工程缺陷分布不均衡和目标多尺度变化的问题,对比其他同类算法拥有更好的精确度和实时性。

This paper proposes a detection algorithm based on an improved you only look once version 5(YOLOv5)model to address the issues of low efficiency and poor accuracy in defect detection of substation roof constructions.Firstly,the algorithm preprocesses images to reduce the impact of external noise on the detection outcomes.Secondly,it introduces an improved self-attention mechanism into the network’s backbone.It replaces the traditional convolutional layers at the end of the YOLOv5 backbone with multi-head self-attention layers to better capture global correlation information.Finally,a cross-layer weighted cascade structure is introduced in the detection phase to integrate surface-level defects′edge and contour information into deeper feature layers,thereby improving the network′s accuracy in defect boundary regression.Experimental results show that the improved YOLOv5 algorithm achieves an average accuracy of 93.2%in defect detection across five types of layers:insulation,isolation,steam barrier,waterproofing,and leveling,with a frame rate of 163.5 frames per second.This solves the problem of uneven defect distribution and varying target scales encountered in practical engineering settings,offering superior accuracy and real-time performance compared to similar algorithms.