Consistent compliance with quality requirements of the product becomes the key to the sustainable competitiveness for wood processing companies competing in domestic and export markets. In modern industrial processes, fast and efficient detection of defects plays a crucial role in quality control. More and more markets become so quality sensitive that even a small number of defective products might lead to recalls of the whole batch and damaged relationship with clients. While the source of non-compliance might be any production process, including all upstream processes before packaging, improved quality inspection systems at the end of the production line to eliminate non-compliant products becomes extremely important. In most industrial processes, the defect detection process still relies on the visual inspection of trained workers with low detection efficiency and precision. In this paper, a Computer Vision system was developed for quality inspection of the wood surface to visually identify quality of product by the smart camera to detect the visual non-compliance. The vision system consists of an inspection camera for capturing the image of the package and an image analysis software with Deep Learning capabilities to identify misplaced, missing and damaged wood areas. Wood defect detection increases the automation of the industry, making it less labour intensive, less costly and with improved efficiency. Summarizing the results, we conclude that the main benefits of the quality control and optimization scanner are primarily an increase in wood processing volume by 21% per month. These speed gains stem from the elimination of manual correction, human error, and decision making. And secondly, it is a decrease in “error costs error” the amount of uselessly processed material, in the example of our case has fallen by about 32%