DEVELOPMENT OF A MACHINE VISION SYSTEM FOR DAMAGE AND OBJECT DETECTION IN TUNNELS USING CONVOLUTIONAL NEURAL NETWORKS

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Abstract. Tunnel inspection, i.e. detection of damages and defects on concrete surfaces, is essential for monitoring structural reliability and health conditions of transport facilities, thus providing safe and sustainable urban transportation infrastructures. In this study, an innovative visual-based system is developed for damage and object detection tasks in roadway tunnels based on deep learning techniques. The main components of the developed Machine Vision System such as industrial cameras, flash-based light sources, controller, the synchronization unit and corresponding software programs are designed to collect high-resolution images with sufficient quality from dimly lit tunnel environments in normal traffic flows with an operating speed of 30–50 km/h. Unlike recent studies, the training data includes multiple types of damage such as cracks, spalling, rust, delamination and other surface changes. Furthermore, 10 classes of common tunnel objects including traffic signs, traffic cameras, traffic lights, ventilation ducts, various sensors and cables are labeled for object detection. As state-of-the-art Convolutional Neural Networks, DeepLab and U-Net are trained and evaluated using accuracy metrics for image segmentation. The results highlight the most important parameters of the discussed Machine Vision System as well as the performance of DeepLab and U-Net for object and damage detection.

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