基于自适应锚框的裂缝目标检测算法研究

Crack Target Detection Algorithm Based on Adaptive Anchor Frame

随着交通的发展,桥梁在运输过程中扮演着越来越重要的角色,桥梁也更加多样化。因此面对大量工况不同的桥梁,发展一种能便捷学习新工况的智能化裂缝检测技术显得尤为重要。为提高目标检测算法的准确率和效率,本文将裂缝原始图像切分成3种不同分辨率和尺寸的切片,训练网络识别不同尺寸的裂缝。同时为了增加算法的后续拓展性,设计一种根据训练集标注尺寸自适应调整锚框的手段,让算法在后续使用过程中针对不同工程情况需要增加训练数据时,能直接添加数据进行训练,自动调整最佳锚框尺寸,使该算法在实际使用中具有学习改进的空间。与原始YOLOv3网络和文献中的算法对比,本文算法的精确度平均达到91%以上且扩展性更好。

With the development of transportation,bridges play an increasingly important role in the transportation process,and bridges are more diversified.Therefore,in the face of a large number of bridges with different working conditions,it is particularly important to develop an intelligent crack detection technology that can conveniently learn new working conditions.To improve the accuracy and efficiency of the target detection algorithm,this paper divides the original crack image into slices with three sizes of resolution,and trains the network to recognize cracks of different sizes.To increase the subsequent expansion of the algorithm at the same time,we design a method to adaptively adjust the anchor box according to the training set dimension,so the algorithm can directly add the data for training and automatically adjust the optimal anchor box size when need to increase the training data for different engineering conditions in subsequent process,which makes the algorithm useful for actually applying.Compared with the original YOLOv3 network and some algorithms in references,the accuracy of the proposed algorithm is 91%on average and the scalability is better.