# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
## 6 Conclusions  
Smart factories are evolving to become intelligent by combining major ICT technologies such as IoT, robots, and AI to connect all processes of production, distribution, service, and consumption. Smart factories for manufacturing intelligence beyond production automation still have many problems to be solved on the production floor to improve productivity, reduce costs, and optimize processes. To respond quickly to flexible production environments and rapidly changing consumer demands, robots, the core equipment of smart factories, require automation and intelligence that can operate on their own without human intervention. The robot's machine vision technology is one of the core technologies of an important smart factory that can automate robots. This study proposes a simple and robust HSV color-space-based object localization algorithm for external environmental changes (illumination interference and shadow, reflections, lighting conditions, etc.) in a highly variable manufacturing environment. The proposed algorithm does not require the user to specify the HSV color in advance for object detection and localization, even if the product is changed. The HSV color of the reference ROI is automatically extracted from the vision of the robot connected to the IoT device. In addition, through random sampling, the ROI merge operation is performed on only the ROI with high HSV similarity to the reference ROI, so that the object localization of the product can be automatically identified within the 3D printer. This means that object localization can be extracted quickly and automatically without user intervention and prior knowledge (e.g., learning and pre-setting). Further work is needed to recognize the position of objects quickly and easily for a variety of products that contain multiple colors, not just a single color. In addition, when HSV color values are distributed differently in the ROI, it is necessary to develop an algorithm that can accurately measure and analyze the distance of HSV similarity by spatially subdividing the ROI. It is necessary to consider the optimization of the HSV color similarity distance and random sampling ROI size. Moreover, interesting research [50; 51; 52] such as artistic robot painting and real-time grasping detection for the intelligence and automation of robots have been reported. We expect that the time and effort required for robot setting and teaching work, which are approached by human manual work, can be reduced. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Object**} & **BS\({}^{\dagger}\) Using** & **BS\({}^{\dagger}\) Using** & **GrabCut** & **HOG** & **Proposed** \\  & **Otsu** & **GMM** & & & **Algorithm** \\ \cline{2-6}  & **IoU** & **IoU** & **IoU** & **IoU** & **IoU** \\ \hline Test Product 1 & 0.1004 & 0.6294 & 0.9733 & 0.8296 & 0.9039 (0.9458) \\ Test Product 2 & 0.9195 & 0.9195 & 0.9420 & 0.7672 & 0.9178 (0.9194) \\ Test Product 3 & 0.6711 & 0.6827 & 0.9323 & 0.8524 & 0.9218 (0.9378) \\ \hline \hline \end{tabular} \({}^{\dagger}\) Background subtraction (BS).  
\end{table} 
Table 1: Summary of test comparison results. **Author Contributions:** Conceptualization, H.-C.K. and Y.-K.K. ; methodology, H.-C.K. and J.-Y.S. ; investigation, H.-N.H., H.-C.B., and H.-C.K. ; data curation, H.-N.H., H.-C.B., and M.-G.K.; writing--original draft preparation, H.-C.K. ; writing--review and editing, Y.-K.K. ; project administration, Y.-K.K. All authors have read and agreed to the published version of the manuscript. This work was supported by the project for manufacturing data commonly used platform R&D, funded by the Korea Ministry of SMEs and Startups in 2021 (S3125303. Development of Ultra Lightweight Platform for Production Process and Safety Management of Small and Medium Manufacturing Companies).