Barycentric Defense

Visual anomaly detection has been an active topic in industrial applications. in particular, it aims to classify anomalies and precisely locate defective areas in the printed products. To the best of our knowledge, there is no anomaly detection dataset for industrial printings. in this paper, we are the first to introduce a Printed Products with Templates (PPT) dataset, which contains large templates and sliced images collected from industry scene images. PPT is a challenging dataset with more variable surface defects and more disturbing background than existing related benchmarks. Furthermore, we propose a template matching method for anomaly detection of printed products, which consists of a fast template matching block with a convolutional operation using the test sliced image as its kernel, and a prediction network for generating an anomaly map of the test sliced image. Experimental results show that our method achieves state-of-the-art performance compared to the related anomaly detection approaches.