An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model

Abstract

During the recent decade, emerging technological and dramatic uses for drones were devised and accomplished, including rescue operations, monitoring, vehicle tracking, forest fire monitoring, and environmental monitoring, among others. Wildfires are one of the most significant environmental threats to wild areas and forest management. Traditional firefighting methods, which rely on ground operation inspections, have major limits and may threaten firefighters’ lives. As a result, remote sensing techniques, particularly UAV-based remotely sensed techniques, are currently among the most sought-after wildfire-fighting approaches. Current improvements in drone technology have resulted in significant breakthroughs that allow drones to perform a wide range of more sophisticated jobs. Rescue operations and forest monitoring, for example, demand a large security camera, making the drone a perfect tool for executing intricate responsibilities. Meanwhile, growing movement of the deep learning techniques in computer vision offers an interesting perspective into the project’s objective. They were used to identify forest fires in their beginning stages before they become out of control. This research describes a methodology for recognizing the presence of humans in a forest setting utilizing a deep learning framework and a human object detection method. The goal of identifying human presence in forestry areas is to prevent illicit forestry operations like illegal access into forbidden areas and illegal logging. In recent years, a lot of interest in automated wildfire identification utilizes UAV-based visual information and various deep learning techniques. This study focused on detecting wildfires at the beginning stages in forest and wilderness areas, utilizing deep learning-based computer vision algorithms that control and then mitigate massive damages to human life and forest management.

Feasibility of Live Video Feed Transmission from Unmanned Aerial Vehicles for Medical Surveillance During the 2022 Montreal Marathon

“…Deep learning models to detect humans in high-traffic areas using video footage from UAVs have been recently developed. 13 With further improvements, these models could be a useful aid in identification of runners in distress in contexts similar to this study.…”

“…Deep learning models to detect humans in high-traffic areas using video footage from UAVs have been recently developed. 13 With further improvements, these models could be a useful aid in identification of runners in distress in contexts similar to this study. Some of the current challenges in expanding the use of UAVs in mass-gathering medicine are the variability in flying conditions and safety concerns.…”

Rider Optimization With Deep Learning Based Image Encryption for Secure Drone Communication

“…Precise collection of data might be highly complicated in an emergency due to the lack of coordinated action by different agencies during the emergency [6]. Nevertheless, it was recommended that to enhance efficiency of the emergency management, recent technologies and methodologies are needed to conceptualize systems that integrate a mixture of spatial/temporal-oriented, telecommunication tools, and remote sensing databases.…”

Retracted: An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”