Work

An Explainable Deep Learning Algorithm for Damage Detection and Classification of Airship Envelopes

Year: 2025

Type: conference-paper

Abstract: Damage data from a specially designed airship envelope test bench were utilized, and a novel explainable deep learning method called Neuron-Focused Class Activation Mapping (NF-CAM) was developed to classify and interpret damage types. Post-training interpretation algorithms were applied to existing classification neural networks to enhance understanding without compromising performance. Feature weights were refined by calculating logit values between similar features, thereby improving focus on target regions. Feature maps were sorted and accumulated to reduce the impact of edges and irrelevant areas, effectively highlighting key feature regions. NF-CAM was employed to re-weight feature maps, while occlusion saliency maps were used to validate the reliability of the emphasized regions. The proposed method demonstrated high accuracy in classifying various damage types. It showed a more significant decrease in accuracy during NF-CAM occlusion experiments compared to mainstream methods, highlighting its superior explanatory capabilities.

DOI: 10.58286/31028

Authors: Fangyuan Zhao, Yiwei Cheng, Wenwei Liu, Yuanhang Wang

Institutions: China University of Geosciences, Shenzhen Technology University

Language: English

Topic: Explainable Artificial Intelligence (XAI)

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province

Awards: G1184675653, G7023856084, G8838971247

OpenAlex ID: W4409730861