The MINSU(Mobile Inventory and Scanning Unit) algorithm uses the
computational vision analysis method to record the residual quantity/fullness
of the cabinet. To do so, it goes through a five-step method: object detection,
foreground subtraction, K-means clustering, percentage estimation, and
counting. The input image goes through the object detection method to analyze
the specific position of the cabinets in terms of coordinates. After doing so,
it goes through the foreground subtraction method to make the image more
focus-able to the cabinet itself by removing the background (some manual work
may have to be done such as selecting the parts that were not grab cut by the
algorithm). In the K-means clustering method, the multi-colored image turns
into a 3 colored monotonous image for quicker and more accurate analysis. At
last, the image goes through percentage estimation and counting. In these two
methods, the proportion that the material inside the cabinet is found in
percentage which then is used to approximate the number of materials inside.
Had this project been successful, the residual quantity management could solve
the problem addressed earlier in the introduction.

MINSU 算法使用计算机视觉分析方法记录柜子的剩余数量 / 充满度。输入图像经过目标检测、前景减法、K-means 聚类、百分比估计和计数等五步处理，可以准确地估算柜子内的材料数量，从而解决前文提到的剩余量管理问题。