Automation and human-robot collaboration are increasing in modern workplaces such as industrial manufacturing. Nowadays, humans rely heavily on advanced robotic devices to perform tasks quickly and accurately. Modern robots with computer vision and artificial intelligence are gaining attention and popularity rapidly. This paper demonstrates how a robot can automatically detect an object’s shape, color, and size using computer vision techniques and act based on information feedback. In this work, a powerful computational model for a robot has been developed that distinguishes an object’s shape, size, and color in real time with high accuracy. Then it can integrate a robotic arm to pick a specific object. A dataset of 6558 images of various monochromatic objects has been developed, containing three colors against a white background and five shapes for the research. The designed system for detection has achieved 99.8% success in an object’s shape detection. Also, the system demonstrated 100% success in the object’s color and size detection with the OpenCV image processing framework. On the other hand, the prototype robotic system based on Raspberry Pi-4B has achieved 80.7% accuracy for geometrical shape detection and 81.07%, and 59.77% accuracy for color recognition and distance measurement, respectively. Moreover, the system guided a robotic arm to pick up the object based on its color and shape with a mean response time of 19 seconds. The idea is to simulate a workplace environment where a worker will ask the robotic systems to perform a task on a specific object. Our robotic system can accurately identify the object’s attributes (e.g., 100%) and is able to perform the task reliably (81%). However, reliability can be improved by using a more powerful computing system, such as the robotic prototype. The article’s contribution is to use a cutting-edge computer vision technique to detect and categorize objects with the help of a small private dataset to shorten the training duration and enable the suggested system to adapt to components that may be needed for creating a new industrial product in a shorter period. The source code and images of the collected dataset can be found at: https://github.com/TituShahoriar/cse499B_Hardware_Proposed_System.

在工业制造等现代工作场所中，自动化和人机协作正在不断增加。如今，人类严重依赖先进的机器人设备来快速准确地执行任务。具有计算机视觉和人工智能的现代机器人正在迅速获得关注和普及。本文演示了机器人如何使用计算机视觉技术自动检测物体的形状、颜色和尺寸，并根据信息反馈采取行动。在这项工作中，开发了一种强大的机器人计算模型，可以高精度地区分物体的形状、大小和颜色。然后它可以集成机械臂来拾取特定物体。已开发出包含 6558 张各种单色物体图像的数据集，其中包含白色背景下的三种颜色和用于研究的五种形状。所设计的检测系统在物体形状检测方面取得了99.8%的成功率。此外，该系统在使用 OpenCV 图像处理框架进行物体颜色和尺寸检测方面表现出 100% 的成功。另一方面，基于Raspberry Pi-4B的原型机器人系统的几何形状检测准确率达到80.7%，颜色识别和距离测量准确率分别达到81.07%和59.77%。此外，该系统还根据物体的颜色和形状引导机械臂拾取物体，平均响应时间为 19 秒。这个想法是模拟工作场所环境，工作人员将要求机器人系统对特定对象执行任务。我们的机器人系统可以准确识别物体的属性（例如，100％）并且能够可靠地执行任务（81％）。然而，可以通过使用更强大的计算系统（例如机器人原型）来提高可靠性。本文的贡献是使用尖端的计算机视觉技术，借助小型私有数据集来检测和分类对象，以缩短训练时间，并使建议的系统能够适应创建新工业产品可能需要的组件在较短的时间内。收集的数据集的源代码和图像可以在以下位置找到：https://github.com/TituShahoriar/cse499B_Hardware_Proposed_System。