MULTIPLE FLYING OBJECT DETECTION USING MACHINE LEARNING ALGORITHM

Abstract [English]

Detecting multiple flying objects is a crucial task in various domains such as surveillance, wildlife monitoring, and airspace management. This paper presents an approach to detect multiple flying objects using object detection algorithms. The process involves several key steps, including data collection and annotation, preprocessing, model selection, training, evaluation, deployment, real-time detection, post-processing, and monitoring. Initially, a diverse dataset containing images or videos with various flying objects is gathered, and annotations are added to label each object with its corresponding class and bounding box coordinates. Preprocessing techniques like resizing, normalization, and augmentation are applied to enhance the dataset. Next, a suitable object detection algorithm is selected, considering factors like performance and computational efficiency. Common choices include YOLO model, depending on the specific requirements of the application. The chosen model is trained using the annotated dataset, fine-tuned, and evaluated using metrics like precision, recall, and mean Average Precision (mAP). Upon satisfactory performance, the model is deployed in the desired environment, integrated with appropriate hardware for real-time detection. Post-processing techniques such as non-maximum suppression (NMS) are applied to refine the detected bounding boxes, ensuring accurate identification of multiple flying objects. Regular monitoring and maintenance are conducted to keep the deployed model up-to-date and effective in dynamically changing environments