Enhanced Deep Convolutional Neural Network for Building Component Detection Towards Structural Health Monitoring

Civil structural component detection is an important stage in structural health monitoring for the interpretation and analysis of damages associated with various structural components. Structural component detection is a challenging task due to the asymmetrical shape of the components. We introduce Gabor filters as an enhancement for a one-stage detector. Generic Gabor filters are generated to replace the lower convolutional filters of the backbone of SSD. Evaluation of the variant SSD detector on structural imagery shows promising detection results. The experimental results show an improvement in accuracy and how fast the variant SSD detector learns. A comparison with some state-of-the-art detectors indicates the SSD with Gabor filters is favorable.