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This paper introduces a post-hoc explanation-based indicator to detect false negatives in binary defect detection networks, addressing the critical need for reliability in safety-critical AI deployments. The method leverages the difference between class-specific and class-agnostic discriminative heatmaps, quantified by the difference in their Intersection over Union (IoU) scores, to generate a reliability score. Experiments on industrial defect detection benchmarks demonstrate the method's effectiveness in identifying false negatives, achieving 100% recall with adversarial enhancement.
A simple difference in IoU scores between class-specific and class-agnostic heatmaps can reliably flag potentially erroneous predictions in industrial defect detection, even achieving 100% recall of false negatives with adversarial enhancement.
The deployment of AI systems in safety-critical domains, such as industrial defect inspection, autonomous driving, and medical diagnosis, is severely hampered by their lack of reliability. A single undetected erroneous prediction can lead to catastrophic outcomes. Unfortunately, there is often no alternative but to place trust in the outputs of a trained AI system, which operates without an internal safeguard to flag unreliable predictions, even in cases of high accuracy. We propose a post-hoc explanation-based indicator to detect false negatives in binary defect detection networks. To our knowledge, this is the first method to proactively identify potentially erroneous network outputs. Our core idea leverages the difference between class-specific discriminative heatmaps and class-agnostic ones. We compute the difference in their intersection over union (IoU) as a reliability score. An adversarial enhancement method is further introduced to amplify this disparity. Evaluations on two industrial defect detection benchmarks show our method effectively identifies false negatives. With adversarial enhancement, it achieves 100\% recall, albeit with a trade-off for true negatives. Our work thus advocates for a new and trustworthy deployment paradigm: data-model-explanation-output, moving beyond conventional end-to-end systems to provide critical support for reliable AI in real-world applications.
