Souhaila Khalfallah
Papers
1
Total Citations
3
H-Index
1
About
Souhaila Khalfallah is a researcher at the forefront of intelligent robotics and autonomous systems, with a core focus on computer vision and real-time object detection for industrial logistics. Her most impactful work, "Object Detection for Autonomous Logistics: A YOLOv4 Tiny Approach with ROS Integration and LOCO Dataset Evaluation" (2024, 3 citations), introduces a lightweight yet powerful detection model tailored for warehouse automation. By integrating the YOLOv4 Tiny algorithm with the Robot Operating System (ROS) and validating it on the specialized LOCO dataset, Khalfallah has demonstrated how resource-efficient AI can be seamlessly deployed on autonomous robots to navigate complex logistics environments. This contribution is particularly significant for bridging the gap between state-of-the-art deep learning and practical, scalable robotics applications. Her approach addresses critical challenges in real-time perception, enabling safer and more efficient warehouse operations. As an emerging voice in applied AI, Khalfallah’s work is laying the groundwork for next-generation autonomous logistics systems, with potential to transform supply chain automation. Her research continues to inspire students and engineers seeking to combine robust vision models with real-world robotic platforms.
Research Focus
Key Achievements
Top Papers
- 1