在使用深度学习技术的实时项目中改进目标检测的策略

Niloofar Abed, Ramu Murugan
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引用次数: 1

摘要

配备目标检测技术并通过物联网(IoT)控制的设备的普及程度和流行程度有所增加,特别是在后冠状病毒时代。神经网络和人工智能的发展与物联网系统相结合,减少了对人力的需求,提高了生产率,在不利的条件下,用户获得了可接受的满意度。因此,从自动驾驶汽车到农作物,这种机制的使用范围在大多数领域都得到了扩展。由于这些技术在跨学科领域的设计和应用,初学者将面临大量复杂的信息。由于YOLO (You Only Look Once)目标检测算法的普及,本文作为红绿灯主题分类的指南,以实际应用的态度,为使用YOLO进行目标检测的爱好者和开发者提供了提高实时项目中目标检测精度的建议解决方案和独家途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Strategies for Improving Object Detection in Real-Time Projects that use Deep Learning Technology
The popularity and prevalence of devices equipped with object detection technology and controllable via the Internet of Things (IoT) have increased, especially in the post-Corona era. The development of neural networks and artificial intelligence by combining them with IoT systems has achieved acceptable satisfaction among users in adverse conditions by reducing the need for manpower and increasing productivity. Therefore, the scope of using such mechanisms has expanded in most fields, from self-driving vehicles to agricultural crops. Beginners will be confronted with a massive amount of complex information as a result of the design and application of such technologies in interdisciplinary fields. Due to the popularity of using the You Only Look Once (YOLO) object detection algorithm, this article provided a guideline as a traffic light subject classification and, offers suggested solutions and exclusive approches to increase the accuracy of object detection in real-time projects with a practical application attitude for the enthusiasts and developers particularly in object detection scenarios by employing YOLO.
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