Integrated Systems Development: Human Detection and Goods Control with TensorFlow and IoT

Sanaa Mehnaz Baichoo, Raed Abdulla, Muhammad Ehsan Rana
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Abstract

This research comprises the implementation methods used to design and develop a human detection system and goods control system. The development of the TensorFlow Machine Learning algorithm for human detection is described in this work. The use of IoT devices, namely ESP32 CAM for data capture, ESP32 for controlling the overall system, and establishing Firebase Database for communication between the TensorFlow development platform, PyCharm, and ESP32 are explained and justified in this paper. The development of the goods control system using ultrasonic sensors and ESP32 as a micro controller, to control the stepper motor, is also explained and justified. Each system was tested individually first before integrating them. Five tests were performed, namely the response time to activate the stepper motor, the human detection accuracy test, the precision of the ultrasonic sensor responsible for height control, the precision of the ultrasonic sensor responsible for motion control, and the stress analysis test of goods lift. The tests present coherent data, but limitations were still found during the testing phase and had to be readjusted before the final integration of both systems.
集成系统开发:利用 TensorFlow 和物联网进行人体检测和货物控制
这项研究包括用于设计和开发人体检测系统和货物控制系统的实施方法。本作品介绍了用于人体检测的 TensorFlow 机器学习算法的开发过程。本文对物联网设备的使用进行了说明和论证,即使用 ESP32 CAM 采集数据,使用 ESP32 控制整个系统,以及建立 Firebase 数据库用于 TensorFlow 开发平台、PyCharm 和 ESP32 之间的通信。本文还对使用超声波传感器和 ESP32 作为微控制器来控制步进电机的货物控制系统的开发过程进行了说明和论证。在整合每个系统之前,首先对其进行了单独测试。共进行了五项测试,即启动步进电机的响应时间、人体检测精度测试、负责高度控制的超声波传感器的精度、负责运动控制的超声波传感器的精度以及货物升降机的应力分析测试。这些测试提供了一致的数据,但在测试阶段仍发现了一些局限性,必须在最终整合两个系统之前进行重新调整。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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