An Embedded System for Image-based Crack Detection by using Fine-Tuning model of Adaptive Structural Learning of Deep Belief Network

TL;DR

This paper presents an adaptive deep belief network model with self-organizing capabilities for crack detection in concrete images, achieving high accuracy and real-time inference on embedded systems for drone applications.

Contribution

It introduces a novel adaptive structural learning method for DBNs that automatically determines optimal network architecture and enhances real-time crack detection on embedded hardware.

Findings

Achieved over 99.4% accuracy on concrete crack datasets.

Enabled real-time inference on embedded GPU systems.

Reduced model size without sacrificing accuracy.

Abstract

Deep learning has been a successful model which can effectively represent several features of input space and remarkably improve image recognition performance on the deep architectures. In our research, an adaptive structural learning method of Restricted Boltzmann Machine (Adaptive RBM) and Deep Belief Network (Adaptive DBN) have been developed as a deep learning model. The models have a self-organize function which can discover an optimal number of hidden neurons for given input data in a RBM by neuron generation-annihilation algorithm, and can obtain an appropriate number of RBM as hidden layers in the trained DBN. The proposed method was applied to a concrete image benchmark data set SDNET 2018 for crack detection. The dataset contains about 56,000 crack images for three types of concrete structures: bridge decks, walls, and paved roads. The fine-tuning method of the Adaptive DBN can show 99.7%, 99.7%, and 99.4% classification accuracy for test dataset of three types of structures. In this paper, our developed Adaptive DBN was embedded to a tiny PC with GPU for real-time inference on a drone. For fast inference, the fine tuning algorithm also removed some inactivated hidden neurons to make a small model and then the model was able to improve not only classification accuracy but also inference speed simultaneously. The inference speed and running time of portable battery charger were evaluated on three kinds of Nvidia embedded systems; Jetson Nano, AGX Xavier, and Xavier NX.