Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Singh, Ujjwal Kumar; Raj, Aryan; Anand, Amritesh; Baghel, Neeraj; Sharma, Vandana; Mallick, Rahul Dev Title A name given to the resource An Optimized Convolutional Neural Network Model for Real-Time Object Detection in Drones Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Artificial Intelligence and Machine Vision, AIMV 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AIMV66517.2025.11203684" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AIMV66517.2025.11203684</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021819959?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021819959?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Singh U.K., Kalinga Institute of Industrial Technology, School of Computer Engineering, Odisha, India; Raj A., Kalinga Institute of Industrial Technology, School of Computer Engineering, Odisha, India; Anand A., Tata Consultancy Services Limited, Lucknow, India; Baghel N., Bennett University, SCSET, India; Sharma V., CHRIST University, Computer Science Department, Bengaluru, India; Mallick R.D., Kalinga Institute of Industrial Technology, School of Computer Engineering, Odisha, India Description An account of the resource The capacity of drones to perform item detection in actual-time is crucial for applications inclusive of surveillance, seek and rescue, and environmental tracking. This look at investigates how convolutional neural networks (CNNs) can beautify object detection in aerial imagery by enhancing both accuracy and speed. CNNs excel at extracting spatial info, permitting drones to apprehend objects even in relatively complicated environments. by adopting light-weight CNN architectures and optimization strategies, we acquire advanced performance with minimum computational requirements, ensuring green operation on embedded drone platforms. Our findings verify that CNN-based fashions considerably decorate detection accuracy and responsiveness, allowing the improvement of smarter and more self reliant drones. 2025 IEEE. Subject The topic of the resource autonomous navigation; Convolutional Neural Networks; drones; object detection; optimization strategies; real-time processing Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152697-9; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online