Drone-Based Face Detection and Collision Avoidance Framework

Drone-Based Face Detection and Collision Avoidance Framework | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 March 2025 V1 Latest version Share on Drone-Based Face Detection and Collision Avoidance Framework Authors : Mostafa Farwiz [email protected] , Mohamed Sabry , Mohamed Hamed , and Hesham Elmahdy Authors Info & Affiliations https://doi.org/10.22541/au.174229678.86681602/v1 177 views 92 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Abstract Drone technological advancements, driven by advances in computer vision and deep learning, have made a variety of applications in urban planning, security, and surveillance possible. Effective face detection and reliable obstacle avoidance are two crucial facets of drone operations that are covered in this study. This model is appropriate for drone platforms with limited resources since it exhibits good accuracy and real-time performance. The major idea is to implement a dual-purpose framework integrating real-time face detection and autonomous collision avoidance for UAV operations. The YOLOv8 model, fine-tuned on the WIDER FACE dataset, is employed for robust face detection, overcoming challenges such as occlusions and varying head poses, and to ensure safe navigation, a stereo camera-based obstacle avoidance system is introduced as a cost-effective alternative to LiDAR. The system utilizes Semi-Global Block Matching (SGBM) and Block Matching (BM) algorithms to generate dense disparity maps, enabling precise per-pixel depth estimation. Together, the advances improve drone-based surveillance systems by incorporating collision avoidance and real-time face detection. Supplementary Material File (drone-based face detection and collision avoidance framework.docx) Download 541.21 KB Information & Authors Information Version history V1 Version 1 18 March 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords collision avoidance face detection sgbm stereo matching uav. yolov8 Authors Affiliations Mostafa Farwiz [email protected] Cairo University Faculty of Computers and Artificial Intelligence View all articles by this author Mohamed Sabry Cairo University Faculty of Computers and Artificial Intelligence View all articles by this author Mohamed Hamed Cairo University Faculty of Computers and Artificial Intelligence View all articles by this author Hesham Elmahdy Cairo University Faculty of Computers and Artificial Intelligence View all articles by this author Metrics & Citations Metrics Article Usage 177 views 92 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Mostafa Farwiz, Mohamed Sabry, Mohamed Hamed, et al. Drone-Based Face Detection and Collision Avoidance Framework. Authorea . 18 March 2025. 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