Improving Accuracy of Object Detection in Autonomous Drones with Convolutional Neural Networks

Increasing use of autonomous drones in such areas as agriculture, disaster response and surveillance means that an effective and precise method of object recognition is becoming more important. In this study, we use Convolutional Neural Networks (CNNs) to bring additional precision to autonomous drone object detecting systems. While conventional machine learning approaches fail miserably in having complicated contexts and real time processing, CNN have sucessfully handles visual identification task. Based on state of the art methods being data augmentation and transfer learning, plus being real time with data augmentation of the raw data on the edge processor, this research introduces a convolutional neural network (CNN) model which has been fine-tuned and can be used for drones. The model is trained and tested on a big dataset of aerial photos acquired by drones. The item classification on this dataset varies from lighting conditions, to weather variables, etc. A considerable increase in the object detection accuracy is found from experimental findings in reducing false positives and improving resilience in dynamic contexts. Furthermore, the suggested model allows for detection with little latency, making it a good model for drones that must be deployed in real-time. Not only does this work contribute to bolstering rapidly growing autonomous drone navigation, it has great promise in environmental monitoring, SAR, and precision agriculture, and other applications. The components of the models are being researched on how scalable they are and how do they scale up on hardware that has limited resources.