Reconocimiento de objetos para el control de drones en el Drone Engineering Ecosystem

Abstract

The main objective of this final degree project is to develop software that allows a drone to identify and classify objects in real time through the use of a camera. To achieve this, advanced computer vision techniques and artificial intelligence algorithms have been used, ensuring the correct integration between the developed software and the drone control system. A specific use case is to guide the movement of the drone following a route marked by objects strategically located on the ground. The methodology used to meet the objectives set out focused on the implementation of an object detection module based on the YOLO (You Only Look Once) algorithm, a convolutional neural network optimised for object detection in real time. The module was developed in Python, and its integration into the Drone Engineering Ecosystem (DEE), a drone control platform, enabled the identification of objects and subsequent decision-making by the drone. During the development process, different YOLOv8 models (v8n, v8s, v8m, v8l, v8x) were selected and evaluated, and then retrained using a proprietary dataset that included classes such as banana, ball, box and backpack. Several tests were performed, both in simulated environments and in a laboratory with a real drone, to measure the accuracy and efficiency of the system. The results were satisfactory, achieving an improvement in object detection compared to pre-trained models, with accuracy increases of up to 53% in some cases. Despite the achievements, the project had limitations, such as the impossibility of implementing object detection on the drone's Raspberry Pi due to technical problems with the library used, which restricted image processing to the ground equipment. In addition, the resolution of the drone's camera was not optimal for detecting small objects, and some false positives were observed that diverted the drone from its route at times. In conclusion, the project demonstrated the effectiveness of integrating an advanced object detection system into the DEE, opening the door to future improvements in model accuracy and drone functionality. Future lines of development are suggested to optimise the system, such as the reduction of false positives and the integration of processing on the Raspberry Pi.