IR Models and the COVID-19 Pandemic: A Comparative Study of Performance and Challenges

This research study investigates the efficiency of different information retrieval (IR) systems in accessing relevant information from the scientific literature during the COVID-19 pandemic. The study applies the TREC framework to the COVID-19 Open Research Dataset (CORD-19) and evaluates BM25, Contriever, and Bag of Embeddings IR frameworks. The objective is to build a test collection for search engines that tackle the complex information landscape during a pandemic. The study uses the CORD-19 dataset to train and evaluate the IR models and compares the results to those manually labeled in the TREC-COVID IR Challenge. The results indicate that advanced IR models like BERT and Contriever better retrieve relevant information during a pandemic. However, the study also highlights the challenges in processing large datasets and the need for strategies to focus on abstracts or summaries. Overall, the research highlights the importance of effectively tailored IR systems in dealing with information overload during crises like COVID-19 and can guide future research and development in this field.

Lightweight Multi-Drone Detection and 3D-Localization via YOLO

In this work, we present and evaluate a method to perform real-time multiple drone detection and three-dimensional localization using state-of-the-art tiny-YOLOv4 object detection algorithm and stereo triangulation. Our computer vision approach eliminates the need for computationally expensive stereo matching algorithms, thereby significantly reducing the memory footprint and making it deployable on embedded systems. Our drone detection system is highly modular (with support for various detection algorithms) and capable of identifying multiple drones in a system, with real-time detection accuracy of up to 77\% with an average FPS of 332 (on Nvidia Titan Xp). We also test the complete pipeline in AirSim environment, detecting drones at a maximum distance of 8 meters, with a mean error of $23\%$ of the distance. We also release the source code for the project, with pre-trained models and the curated synthetic stereo dataset.