Damage detection of grotto murals based on lightweight neural network

Abstract

Non-contact damage detection of grotto murals is a challenging task, due to their minor cracks and subtle defects. In this paper, a lightweight neural network-based grotto mural damage detection algorithm, named Ghost-C3SE YOLOv5, is proposed, which can effectively reduce the network size while obtaining reasonable damage detection results. First, based on the YOLOv5 detection algorithm, the dimension of the convolution layer is reduced, by adjusting the network structure and integrating a Ghost module. Second, the channel attention mechanism is introduced into the feature extraction backbone network, to adjust the weight of each feature according to its importance, which can accelerate the convergence speed of the loss function during the model training process. Finally, the experiment results have shown that the lightweight model Ghost-C3SE YOLOv5, applied on the Pascal Voc dataset, reduces the number of parameters by about 22.55 million while ensuring the detection precision and recall rate. Also, the training time and the model size are reduced by 36.21% and 46.04%, respectively. Precision is increased by 1.29% and recall remains comparative, while the utilization rate of the GPU is improved by 41.55%. This has addressed the shortcomings of laborious process and low accuracy in manual detection of the grotto murals. Furthermore, a real-time detection performance of 44.86 FPS is achieved here for damage detection of murals in the YunGang Grottoes, which has overcome the drawbacks of high model complexity, high computational cost, slow detection speed and low memory utilization of various existing deep learning algorithms.