Enhancing Autonomous Drone Navigation: YOLOv5-Based Object Detection and Collision Avoidances

Enhancing Autonomous Drone Navigation: YOLOv5-Based Object Detection and Collision Avoidances

Kalrav Gediya1*, Pooja Bhatt2*, Swati Sharma3*, Kalpesh Jadav4*

1Research Scholar, Computer Engineering Department, PIET, Parul University, Vadodara, Gujarat, India

2 Assistant Professor, Computer Engineering Department, PIET, Parul University, Vadodara, Gujarat, India

3Assistant Professor, Computer Engineering Department, PIET, Parul University, Vadodara, Gujarat, India

4Associate Professor, Electronics & Communication Engineering Department, PIET, Parul University, Vadodara, Gujarat, India
1*2303032010011@paruluniversity.ac.in,  2pooja.bhatt28403@paruluniversity.ac.in, 3swati.sharma33111@paruluniversity.ac.in, 4kalpesh.jadav@paruluniversity.ac.in

Abstract

In recent years, drone technology has seen profound advancements, especially with regards to safe and autonomous operation, which heavily relies on object detection and avoidance capabilities. These autonomously functioning drones can operate in challenging environments for tasks like search and rescue operations, as well as industrial monitoring. The present research focuses on enhancing object detection for autonomous drones by utilizing publicly available image datasets instead of custom images. Datasets like VisDrone, DroneDeploy, and DOTA contain a plethora of stunning, real-life images that make them ideal candidates for improving the accuracy and robustness of object detection models. We propose an optimized method for training the YOLOv5 model to enhance object detection. The collected dataset undergoes evaluation from  precision, recall, F1-score, and mAP through both CNN and YOLO models. The findings show that using YOLOv5 deep architecture to implement real-time object detection and avoidance in UAVs is more efficient than traditional CNN approaches.

Keywords: Autonomous Drone, CNN, Yolo V5, Sensor Integration, Deep Learning.