Evaluation of Deep Network-based Methods for Crack Detection of Iron Ore Green Pellet

Crack detection for iron ore green pellet is an essential step in the measuring process of drop strength, which is one of the important quality metrics of green pellet. However, current method for crack detection of green pellet is manual inspection, which is rather laborious, tedious and subjective. Although various deep network-based methods are proposed to automatically detect cracks in tunnel, pavement and wall, little effort has been made on pellet crack detection. Therefore, it is still unknown whether the current deep network-based methods can solve the crack pellet detection problem. In the present work, we perform comparison study to evaluate the performance of six state-of-the-art deep networks, using our green pellet dataset with various crack types and complex background. Comprehensive comparatives are conducted to evaluate the performance and computing efficiency of six deep networks on pellet crack detection. Moreover, task-driving comparison is performed to show what to extent the six deep networks affect the measuring accuracy of drop strength. Our experimental analyses demonstrate that CrackSegNet achieves better crack detection accuracy than other five networks (DeepCrack-Z, DeepCrack-L, U-net, CrackSegNet, GCUnet), and thereby performs better in the task of drop strength measurement. However, computing time needed by CrackSegNet (0.26 seconds per image) is longer than other networks (0.05-0.20 seconds per image) in processing one image with the size of 512x512. In future work, the performance of deep networks needs to be improved in crack detection accuracy as well as computing efficiency to ensure more accurate and fast measurement of pellet quality.