Image Processing-Based Chip Detection by KIZKI Algorithm and Its Optimization

The process of producing aircraft parts involves the drilling of aluminum alloys. This creates a large amount of chips, which are removed using air, but sometimes they still remain within the holes. This is checked by inspectors through visual inspection. However, the quality of human inspection varies based on skill level and fatigue. Thus, image-based inspection should be used to stabilize and further improve inspection quality. This study aims to build a framework for chip detection based on image processing. Taking into account on-site implementation, the system must have low installation and running costs and be standalone. Therefore, we adopt the KIZKI algorithm, which satisfies these conditions. KIZKI means awareness in Japanese. This is a model of human peripheral vision and saccades. It does not require training like AI and can achieve high-speed and high-performance detection using a low-performance computer. In other words, there is no need for a computer with an expensive and high-performance GPU. However, the issue with this algorithm is that multiple hyperparameters must be set manually using trial-and-error. This task is tedious and should be automated. Therefore, this study also aims to optimize the hyperparameter group by using differential evolution (DE), a form of evolutionary computation. In the experiment, this proposed framework was quantitatively evaluated using a constructed image dataset. This dataset consists of 10 normal images without chips and 90 abnormal images with chips. The results showed successful classification in all images. In addition, an average processing time of 40 ms was achieved on a typical laptop.