Deterioration Level Estimation Based on Convolutional Neural Network Using Confidence-Aware Attention Mechanism for Infrastructure Inspection

• Published in: Sensors (Basel, Switzerland) Switzerland), 2022-01, Vol.22 (1), p.382
• Main Authors: Ogawa, Naoki, Maeda, Keisuke, Ogawa, Takahiro, Haseyama, Miki
• Format: Article
• Language: English
• Subjects: attention map; confidence; convolutional neural network; deterioration level estimation; Feature maps; Infrastructure; Infrastructure (Economics); infrastructure inspection; Inspection; Inspections; Neural networks; Neural Networks, Computer; Research Design
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22010382

This paper presents deterioration level estimation based on convolutional neural networks using a confidence-aware attention mechanism for infrastructure inspection. Spatial attention mechanisms try to highlight the important regions in feature maps for estimation by using an attention map. The attention mechanism using an effective attention map can improve feature maps. However, the conventional attention mechanisms have a problem as they fail to highlight important regions for estimation when an ineffective attention map is mistakenly used. To solve the above problem, this paper introduces the confidence-aware attention mechanism that reduces the effect of ineffective attention maps by considering the confidence corresponding to the attention map. The confidence is calculated from the entropy of the estimated class probabilities when generating the attention map. Because the proposed method can effectively utilize the attention map by considering the confidence, it can focus more on the important regions in the final estimation. This is the most significant contribution of this paper. The experimental results using images from actual infrastructure inspections confirm the performance improvement of the proposed method in estimating the deterioration level.