Publications

2025

1. CVPR

   PineSORT: A Simple Online Real-Time Tracking Framework for Drone Videos in Agriculture

   Danny Xie-Li and Fabian Fallas-Moya

   In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2025

   Abs DOI Bib

   We introduce PineSORT, a novel Multiple Object Tracking (MOT) system for drone-based agricultural monitoring, specifically tracking pineapples for yield estimation. Our approach tackles key challenges such as repetitive patterns, similar object appearances, low frame rates, and drone motion effects. PineSORT enhances tracking accuracy with motion direction cost, camera motion compensation, a three-stage association strategy, and overlap management. To handle large displacements, we propose an ORB-based camera compensation technique that significantly improves Association Accuracy (AssA). Evaluated via 5-fold cross-validation against BoTSORT and AgriSORT, PineSORT achieves statistically significant gains in Identity-Switch Penalized IDF1 (ISP-IDF1), IDF1, HOTA, and AssA metrics, confirming its effectiveness for tracking low-FPS drone footage and its value for precision agriculture.

   @inproceedings{11147940,
     author = {Xie-Li, Danny and Fallas-Moya, Fabian},
     booktitle = {2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
     title = {PineSORT: A Simple Online Real-Time Tracking Framework for Drone Videos in Agriculture},
     year = {2025},
     volume = {},
     number = {},
     pages = {65-74},
     keywords = {Accuracy;Costs;Scalability;Cameras;Vectors;Robustness;Yield estimation;Object tracking;Drones;Videos;deep learning;agriculture;video;sort},
     doi = {10.1109/CVPRW67362.2025.00012},
   }

2024

1. BIP

   Simple Object Detection Framework without Training

   Danny Xie-Li, Fabian Fallas-Moya, and Saul Calderon-Ramirez

   In 2024 IEEE 6th International Conference on BioInspired Processing (BIP), 2024

   Abs DOI Bib

   This research introduces a simple framework for Object Detection (OD) based on few-shot methods and Visual Foundation Models (VFM). The framework comprises of three core modules: (i) object proposal, (ii) embedding creation, and (iii) object classification. We evaluated six distinct VFMs to generate the object proposals. We compared the performances of four feature extractors to optimize the object representation, including convolutional neural networks and transformer-based models. Furthermore, we investigated four few-shot methods for classifying objects using minimal labeled data. Our framework provides a scalable and cost-effective solution, specifically applied to OD for pineapple localization in the drone imagery of large pineapple fields, where labeled data are scarce and expensive.

   @inproceedings{10885396,
     author = {Xie-Li, Danny and Fallas-Moya, Fabian and Calderon-Ramirez, Saul},
     booktitle = {2024 IEEE 6th International Conference on BioInspired Processing (BIP)},
     title = {Simple Object Detection Framework without Training},
     year = {2024},
     volume = {},
     number = {},
     pages = {1-6},
     keywords = {Training;Visualization;Biological system modeling;Object detection;Transformer cores;Feature extraction;Transformers;Proposals;Convolutional neural networks;Drones;visual foundational models;few-shot;object detection},
     doi = {10.1109/BIP63158.2024.10885396},
   }

2023

1. TEC

   Artificial Intelligence in STEM Education: Interactive Hands-on Environment using Open Source Electronic Platforms

   Danny Xie Li and Esteban Arias Méndez

   Tecnologı́a en Marcha, 2023

   Abs Bib PDF

   This article describes an interactive methodology to teach Artificial Intelligence (AI) through the contructivism philosophy of learning by doing, using, open source electronic platforms, like Arduino, Snap Circuits, Raspberry Pi and Circuit Playground, with an interactive hands-on approach Workshops. These are provided to high school and non-engineering students by (previously trained) engineering students volunteers. The methodology proposed is designed to highlight, in different learning activities, key concepts about Artificial Intelligence (AI). AI abstractes the human intelligence processes through algorithms and computer systems, taking advantage of the amount of data generated nowadays to create innovative, effective, efficient, accurate and at low cost solutions, applied in different fields. The main purpose is to motivate the participants to explode its creativity, improving their innovation skills to provide solutions for XXI century problems, better quality of life, health, among others. A survey will be conducted for the students to find insights about effectiveness of the proposed methodology to better acquire knowledge about AI knowledge. We encourage instructors to use similar interactive hands-on methodologies and to include AI concepts with STEM activities into general education courses. Other concerns of AI, is about the fairness of these algorithms, the inclusion and diversity is a key player in how these systems are built, and it can have consequences as the person perspective when building it The idea of the need for diversity and inclusion of the AI field.

   @article{li2023artificial,
     title = {Artificial Intelligence in STEM Education: Interactive Hands-on Environment using Open Source Electronic Platforms},
     author = {Li, Danny Xie and M{\'e}ndez, Esteban Arias},
     journal = {Tecnolog{\'\i}a en Marcha},
     volume = {36},
     number = {6},
     pages = {45--52},
     year = {2023},
     publisher = {Editorial Tecnol{\'o}gica de Costa Rica},
     url = {https://dialnet.unirioja.es/servlet/articulo?codigo=9046780},
   }

2. BIP

   Evaluation of the Influence of Multispectral Imaging for Object Detection in Pineapple Crops

   Manfred Gonzalez-Hernandez, Fabian Fallas-Moya, Werner Rodriguez-Montero, and 5 more authors

   In 2023 IEEE 5th International Conference on BioInspired Processing (BIP), 2023

   Abs DOI Bib

   Normally, most studies related to Object Detection focus only on RGB images. However, this research explores the feasibility of utilizing multispectral drone images, incorporating RGB channels with near-infrared, and red-edge channels, to perform Object Detection (OD) using drone images of pineapple crops. There are two main challenges when dealing with multi-spectral images. The first challenge is related to the alignment of the images when dealing with different cameras. Multispectral image alignment corrects for camera position and exposure time differences. We use SIFT and ORB for feature-based exposure matching after initial phase alignment. The second challenge is how to incorporate the extra channels into the RGB images, also known as channel fusion. Here, we studied two fusion techniques: early and late fusion. These techniques offer a comprehensive perspective on the potential of …

   @inproceedings{10379335,
     author = {Gonzalez-Hernandez, Manfred and Fallas-Moya, Fabian and Rodriguez-Montero, Werner and Xie-Li, Danny and Roman-Solano, Bryan and Corrales-Garro, Francini and Sadovnik, Amir and Qi, Hairong},
     booktitle = {2023 IEEE 5th International Conference on BioInspired Processing (BIP)},
     title = {Evaluation of the Influence of Multispectral Imaging for Object Detection in Pineapple Crops},
     year = {2023},
     volume = {},
     number = {},
     pages = {1-6},
     keywords = {Multispectral imaging;Crops;Vegetation mapping;Object detection;Cameras;Indexes;Drones;multispectral;object detection;deep learning},
     doi = {10.1109/BIP60195.2023.10379335},
   }

3. IHTC

   The Women’s Antenna: An experience of community technology construction led by Cabécar women of Costa Rica

   Kemly Camacho, Elizabeth Herrera, Danny Xie-Li, and 1 more author

   In 2023 IEEE International Humanitarian Technology Conference (IHTC), 2023

   Abs DOI Bib

   The article delves into the experiences of three remarkable women leaders, Mina A., Mina X., and Mina S., within Costa Rica’s Cabécar indigenous community in Alto Pacuare. Referred to as “Mina,” these women play integral roles in decision-making despite their remote locations. Their initiation of the Association of Cabécars Women of Alto Pacuare led to impactful ventures, including the establishment of the “Casa de las Mujeres” (House of Women). Nonetheless, they express apprehensions about the potential internet-induced impact on their age-old culture.Participating in a hackathon organized by Sulá Batsú Cooperative, these leaders proposed a platform aimed at preserving ancestral wisdom. Bolstered by institutional backing, they developed a community network merging natural metaphors to bridge the digital divide.This innovative “walkie talkie” style network seeks to fuse Cabécars women’s traditional …

   @inproceedings{10508857,
     author = {Camacho, Kemly and Herrera, Elizabeth and Xie-Li, Danny and Arias-Méndez, Esteban},
     booktitle = {2023 IEEE International Humanitarian Technology Conference (IHTC)},
     title = {The Women’s Antenna: An experience of community technology construction led by Cabécar women of Costa Rica},
     year = {2023},
     volume = {},
     number = {},
     pages = {1-7},
     keywords = {Training;Technological innovation;Shape;Decision making;Radio networks;Global communication;Cultural differences;Community networks;Costa Rica;SDGs},
     doi = {10.1109/IHTC58960.2023.10508857},
   }

2020

1. NeurIPS

   ML4H Auditing: From Paper to Practice

   Luis Oala, Jana Fehr, Luca Gilli, and 12 more authors

   In Proceedings of the Machine Learning for Health NeurIPS Workshop, 11 dec 2020

   Abs Bib PDF Video

   Healthcare systems are currently adapting to digital technologies, producing large quantities of novel data. Based on these data, machine-learning algorithms have been developed to support practitioners in labor-intensive workflows such as diagnosis, prognosis, triage or treatment of disease. However, their translation into medical practice is often hampered by a lack of careful evaluation in different settings. Efforts have started worldwide to establish guidelines for evaluating machine learning for health (ML4H) tools, highlighting the necessity to evaluate models for bias, interpretability, robustness, and possible failure modes. However, testing and adopting these guidelines in practice remains an open challenge. In this work, we target the paper-to-practice gap by applying an ML4H audit framework proposed by the ITU/WHO Focus Group on Artificial Intelligence for Health (FG-AI4H) to three use cases: diagnostic prediction of diabetic retinopathy, diagnostic prediction of Alzheimer’s disease, and cytomorphologic classification for leukemia diagnostics. The assessment comprises dimensions such as bias, interpretability, and robustness. Our results highlight the importance of fine-grained and caseadapted quality assessment, provide support for incorporating proposed quality assessment considerations of ML4H during the entire development life cycle, and suggest improvements for future ML4H reference evaluation frameworks.

   @inproceedings{pmlr-v136-oala20a,
     title = {ML4H Auditing: From Paper to Practice},
     author = {Oala, Luis and Fehr, Jana and Gilli, Luca and Balachandran, Pradeep and Leite, Alixandro Werneck and Calderon-Ramirez, Saul and Li, Danny Xie and Nobis, Gabriel and Alvarado, Erick Alejandro Mu\~noz and Jaramillo-Gutierrez, Giovanna and Matek, Christian and Shroff, Arun and Kherif, Ferath and Sanguinetti, Bruno and Wiegand, Thomas},
     booktitle = {Proceedings of the Machine Learning for Health NeurIPS Workshop},
     pages = {280--317},
     year = {2020},
     editor = {Alsentzer, Emily and McDermott, Matthew B. A. and Falck, Fabian and Sarkar, Suproteem K. and Roy, Subhrajit and Hyland, Stephanie L.},
     volume = {136},
     series = {Proceedings of Machine Learning Research},
     month = {11 Dec},
     publisher = {PMLR},
     url = {https://proceedings.mlr.press/v136/oala20a.html},
   }