Lightweight detection network for bridge defects based on model pruning and knowledge distillation

Deep learning techniques improve the detection performance of objects by expanding the depth of network and increasing the complexity of model structure, however, this makes it difficult to deploy as an application on embedded devices. In this study a lightweight model was put forward to better identify bridge defects combining with two advanced feature fusion modules to increase the multi-scale information perception and extraction capability of the network. Sparse training and slimming pruning were used to remove redundant parameters. Then, fine-tuning and knowledge distillation (KD) enabled the network to recover its detection accuracy and robustness. Furthermore, the unsupervised learning CycleGAN was used to enhance the generalization performance of the dataset and improve the detection accuracy. The experimental results show that compared with the original model, the precision rate, mAP@ 0.5 and detection speed (FPS) is increased by 8.07%, 6.15% and 85.06%, respectively. Moreover, the model volume of proposed network is 43.04% of the original ones. The results indicates that the proposed network can be deployed on devices with limited storage to achieve accurate and real-time detection of bridge defects. These findings represent a major step forward in the automation of detection and quantification of structural defects.