An Optimized Convolutional Neural Network Model for Real-Time Object Detection in Drones
autonomous navigation; Convolutional Neural Networks; drones; object detection; optimization strategies; real-time processing
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.
Singh, Ujjwal Kumar; Raj, Aryan; Anand, Amritesh; Baghel, Neeraj; Sharma, Vandana; Mallick, Rahul Dev
2025 International Conference on Artificial Intelligence and Machine Vision, AIMV 2025;
Institute of Electrical and Electronics Engineers Inc.
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1109/AIMV66517.2025.11203684 https://www.scopus.com/pages/publications/105021819959?origin=resultslist
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