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This paper describes a method for the classification of bank notes. The algorithm has three stages, and classifies bank notes with v ery low error rates and at high speeds.To achiev e the v ery low error rates, the result of classification is check ed in the final stage by using different features to those used in the first two. High-speed processing is mainly achiev ed by the hierarchical structure, which leads to low computational costs. I n ev aluation on 32, 850 samples of US bank notes, with the same number used for training, the algorithm classified all samples precisely with no error sample.

Automatic crack detection is a critical task that has the potential to drastically reduce labor-intensive building and road inspections currently being done manually. Recent studies in this field have significantly improved the detection accuracy. However, the methods often heavily rely on costly annotation processes. In addition, to handle a wide variety of target domains, new batches of annotations are usually required for each new environment. This makes the data annotation cost a significant bottleneck when deploying crack detection systems in real life. To resolve this issue, we formulate the crack detection problem as a weakly-supervised problem and propose a two-branched framework. By combining predictions of a supervised model trained on low quality annotations with predictions based on pixel brightness, our framework is less affected by the annotation quality. Experimental results show that the proposed framework retains high detection accuracy even when provided with low quality annotations. Implementation of the proposed framework is publicly available at https://github. com/hitachi-rd-cv/weakly-sup-crackdet.

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