Crack Image Detection and Edge Feature Detection by Introducing Lightweight Network
DOI:
https://doi.org/10.62051/sqz1zk59Keywords:
Lightweight network; Crack image detection; Edge features; Multi-scale supervision; Convolutional Neural Network.Abstract
In order to optimize the crack image detection technology and realize edge feature detection, an edge detection model based on Visual Geometry Group (VGG) algorithm is constructed based on multi-scale supervised model and convolutional neural network (CNN). Then, a lightweight network is introduced to optimize the model, and a significant analysis is made with the classical algorithm. Compared with the mean absolute error (MAE), the performance advantage of the optimization algorithm is further verified. The research results show that the edge detection algorithm of multi-scale supervised model proposed in this study is superior to the traditional algorithm in all datasets. The F-measure values in four different datasets are 0.86, 0.93, 0.93 and 0.88 respectively, which shows the superior performance of the algorithm. Meanwhile, the MAE values of the algorithm based on lightweight network optimization in four different datasets are 0.04, 0.03, 0.03 and 0.06 respectively. Compared with other classical algorithms, the optimization algorithm has achieved the lowest MAE values, which proves that the optimization algorithm has significant advantages and higher accuracy in edge detection tasks. This study improves the accuracy and efficiency of crack image detection, which is of great significance to promote the development and application of crack image detection technology.
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