Super-Resolution Images Methodology Applied to UAV Datasets to Road Pavement Monitoring

Inzerillo, Laura; Acuto, Francesco; Mino, Gaetano Di; Uddin, Mohammed Zeeshan

doi:10.3390/drones6070171

Cited by 25 publications

(20 citation statements)

References 66 publications

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“…Drones take on-demand images from a low-ying aircraft for a number of reasons, including emergency product delivery, border enforcement, accident rescues, and visual surveillance for population protection. [1]. e prospect of market expansion for vision processing within drones or commercial aerial vehicles increases the overall number of automobiles.…”

Section: Introductionmentioning

confidence: 99%

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

Refaai

Rinku

Thamarai³

et al. 2022

Advances in Materials Science and Engineering

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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.

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Section: Introductionmentioning

confidence: 99%

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

Refaai

Rinku

Thamarai³

et al. 2022

Advances in Materials Science and Engineering

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“…The article authored by Inzerillo et al (2022) describes the use of UAVs for detecting structural cracks in concrete structures and road pavement surfaces [56]. According to the authors, the quality of images collected by UAVs can be affected by vibrations and distances, thus resulting in a loss of critical information that makes crack detection difficult.…”

Section: Detailed Descriptionmentioning

confidence: 99%

“…By enhancing the quality of low-resolution images, the super-resolution approach effectively enables the detection and measurement of pavement distresses such as cracks and potholes. This, in turn, provides invaluable information for the formulation of maintenance strategies [56].…”

Section: Detailed Descriptionmentioning

confidence: 99%

Pavement Inspection in Transport Infrastructures Using Unmanned Aerial Vehicles (UAVs)

Feitosa,

Santos,

Almeida

2024

Sustainability

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The growing demand for the transportation of goods and people has led to an increasing reliance on transportation infrastructure, which, in turn, subjects the pavements to high traffic volumes. In order to maintain adequate service and safety standards for users, it is essential to establish effective maintenance strategies that ensure the preservation of pavement conditions. As a result, emerging innovations in pavement surface inspection methods, surpassing traditional techniques in terms of inspection and data processing speed and accuracy, have garnered significant attention. One such groundbreaking innovation in inspection systems that has been tested and used in recent years to assess infrastructure condition is the use of unmanned aerial vehicles (UAVs). This study aims to present a critical open-access literature review on the use of UAVs in the inspection of transportation infrastructure pavement in order to assess the type of equipment used, the technology involved, applicability conditions, data processing, and future evolution. The analysis of relevant literature suggests that the integration of intelligent technologies substantially enhances the accuracy of data collection and the detection of pavement distress. Furthermore, it is evident that most applications and research efforts are oriented towards exploring image processing techniques for the creation of 3D pavement models and distress detection and classification.

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“…This is a key factor in restoring 3D models saturated with realistic details from scenes collected by this powerful acquisition device [8]. The technical development of drone camera sensors has provided new capabilities for capturing aerial information in the form of high-resolution images from different destinations, angles, and heights [9]. allowing the collection of multiple images of the human scene, from which data sets can be generated that are considered as training inputs for many algorithms and applications.…”

Section: Introductionmentioning

confidence: 99%

PIXGAN-Drone: 3D Avatar of Human Body Reconstruction From Multi-View 2D Images

Salim Rasheed,

Jabberi,

Hamdani

et al. 2024

IEEE Access

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Research on training Generative Adversarial Networks (GANs) to create 3D human body avatars from 2D datasets is underway. Research done in this field shows promise and has paved the way for significant advancements in a variety of applications, including virtual reality, sports analysis, cinematography, surveillance, and cinematography. By avoiding obstacles and producing high-resolution, rich-information multi-view (RGB) images, drone active tracking combined with aerial photography sensors can eliminate occlusions and enable 3D avatar body reconstruction. Due to several issues, such as restricted perspective coverage, obvious occlusions, and texture disappearance, 3D avatar reconstruction techniques encounter training failures that cause distortions and feature loss in 3D reconstructed models. The new endto-end trainable deep neural network methodology PIXGAN-Drone is presented in this paper for the photorealistic 3D avatar of the human body reconstruction from multi-view images. It is based on the integration of active tracking drones equipped with aerial photography sensors (stable automatic circular motion system) into the Pix2Pix GANs training framework to generate high-resolution 2D models. Conditional image-toimage translation and dynamic aerial perspectives can be used to develop realistic and accurate 3D models. This research conducted experiments on multiple datasets to demonstrate the improved performance of our method over state-of-the-art methods for various metrics (Chamfer, P2S, and CED). The results showed that our 3D reconstructed human avatars were 0.0293, 0.0271, and 0.0232 on RenderPeople, 0.0133, 0.0136, 0.0050 on People Snapshot (indoor), 0.0154, 0.0101, 0.0063 on People Snapshot (outdoor), and 0.0316, 0.0275, 0.0216 on Custom data-drone (collected dataset).INDEX TERMS 3D human avatar, 3D reconstruction, PIX2PIXGAN, body model rendering, drone active tracking.

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Super-Resolution Images Methodology Applied to UAV Datasets to Road Pavement Monitoring

Cited by 25 publications

References 66 publications

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

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

Pavement Inspection in Transport Infrastructures Using Unmanned Aerial Vehicles (UAVs)

PIXGAN-Drone: 3D Avatar of Human Body Reconstruction From Multi-View 2D Images

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