Strategies for Improving Object Detection in Real-Time Projects that use Deep Learning Technology
Niloofar Abed, M. Ramu
Abstract
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.