A Fully Convolutional Neural Network Model Towards Internet of Things-Enabled Crack Detection in useful Structure: An Application to Structural Health Monitoring
Keywords:
Deep Learning; Structural Health Monitoring; Bridge Crack Detection; Image Classification;Abstract
A highly advanced fully convolutional neural network (CNN) model is methodically proposed to classify bridge cracks. This paper explored Python libraries to create a simulation and training platform for the model. The proposed approach is observed to be an outstanding model for identifying bridge cracks effectively having comparatively less complex training with accuracy rates well over 90 percent and it is 82 percent efficient than the other compared approach. Here, intelligent detection methods have been proposed to optimize the bridge safety efficacy mitigating the associated risk factors. Moreover, in this study the significant impact of integrating IoT technology in structural health monitoring, especially in bridge crack detection has been highlighted.
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