Evolutionary Computation in Machine Vision for Manufacturing and Logistics Industry

The aim of this research is to explore how evolutionary approaches can be used to solve vision based problems, as well as tackling problems in the manufacturing and logistics industries. It can be considered as a generic approach to solve other vision-based problems such as inspection, monitoring, guidance and navigation problems in different areas. Benchmarking methods and exploratory case study approaches are adopted as the research method. Although the attempt is mainly research-oriented, particular care has been given to test the feasibility of the proposed approaches, in the thesis, at a level which makes them accessible to researchers and industrial practitioners. The proposed algorithms outperform other algorithms in both the benchmark tests, in template matching and in circle detection problems. The tests and simulations have revealed the efficiency and feasibility of the proposed algorithms in solving numeric optimization problems as well as vision-based problems. To validate the feasibility of the proposed approaches, two case studies concerned with printed circuit board manufacturing and pick-and-pack processes have been conducted. The deliverables of this research not only provide the design of the proposed system which will support efficient quality decision making and improve operational performance, but also open the door to incorporating evolutionary computation techniques in future machine vision systems that can be applied to different areas.