A Modified CenterNet for Crack Detection of Sanitary Ceramics

In this paper, we propose a modified CenterNet to complete the defect detection of Sanitary Ceramics. Generally, visual quality inspection is rather important during the productive process of Sanitary Ceramics and it is nearly impossible to inspect the massive images by hand. Consequently, it is necessary to devise an accurate and real-time system to process the data. However, due to the varied shapes and backgrounds of ceramics, conventional computer vision methods are usually not robust to all those variables. Detectors based on Deep Learning start to be adopted in recent years, but most algorithms require some carefully devised anchor boxes and post-processing methods, which also bring more computational costs. Here we decide to take advantage of the anchor-free model, CenterNet. We change the main structure to fit our own data and introduce an extra branch with shallow layers to strengthen the feature representation. The results have shown the great power of this model. Without even any post-processing methods, our model achieves a result of 96.16 AP on the established dataset.