DLSU Center for Computational Imaging and Visual Innovations

🏅 Outstanding Thesis Award – Silver Medal 🏅

Congratulations to Charles Joseph Hinolan, Mac Andre Javellana, Mari Salvador Lapuz, and Audrea Arjaemi Tabadero for receiving the Outstanding Thesis Award (Silver Medal) for their work titled: "A Lightweight Computer Vision Model for Camouflaged Crop Detection."

This marks the 2nd Outstanding Thesis Award of our lab in the BSCS program and our 3rd award overall — a testament to the consistency and impact of our students' research.

🌱 About the Research
Detecting camouflaged crops remains a major challenge in agricultural computer vision, especially when deploying models on resource-constrained edge devices.

The team conducted a systematic evaluation of lightweight design strategies, such as backbone replacement, pruning, and knowledge distillation across SSD, YOLOv8l, and RT-DETR architectures using various datasets. Their findings highlight key trade-offs between detection accuracy (mAP) and computational efficiency, offering practical insights for real-time agricultural deployment.

This work contributes meaningful guidance toward optimizing object detection systems for real-world agricultural environments where efficiency and accuracy must go hand in hand.

We are incredibly proud of the team for pushing the boundaries of applied computer vision in agriculture. 🚜📊

RESEARCH MODE: ON. ✍️🔥

The DLSU Center for Computational Imaging and Visual Innovations, under the DLSU Advanced Research Institute for Informatics, Computing and Networking, successfully held Focused Research and Manuscript Enhancement (FRAME) Sprint – Day 1 at the John Gokongwei Innovation Center, DLSU Laguna Campus, on January 27, 2026 for BSMSCS students and on February 16, 2026 for THS-ST3 students.

The FRAME Sprint is designed to provide high-focus time dedicated to strengthening research outputs and refining conference manuscripts, with the goal of making each paper submission-ready for reputable conferences and journals. This will be a recurring event throughout the term.

Through guided feedback, structured writing sessions, and collaborative review, our students worked intensively on sharpening their problem statements, clarifying methodologies, strengthening results analysis, and improving overall technical writing quality.

More drafts polished. More papers closer to submission. 🚀📄

DLSU Center for Computational Imaging & Visual Innovations (CIVI)

CIVI 2526T1 Academic Lecture Series
📆 November 12, 2025 (W)
⏰ 1:00 PM
📹 Online via Zoom
📱 Register here: https://forms.gle/W9LxYpgpKrSn39HK9

Talk 1: An Introduction to Camouflaged Object Detection
Talk 2: Introduction to Single-Stage Object Detection
Talk 3: Pruning in Computer Vision: Making Object Detection Lighter
Talk 4: Curriculum Learning for Image Classification

Talk 1: An Introduction to Camouflaged Object Detection
ABSTRACT
This lecture will cover Camouflaged Object Detection (COD), a field of Computer Vision that focuses on segmenting camouflaged objects hidden in images. Designed for advanced learners, this lecture will cover the history and prominent trends in COD, as well as the notable networks and metrics used in the field.

The outline of the lecture is as follows:
1. Definition of COD
2. History and Creation of COD
3. COD Network Architectures
4. COD Performance Metrics
5. Current Applications for COD

Prerequisite knowledge
Attendees should be knowledgeable about these topics:
- Understanding of Neural Network Concepts (e.g., weights, loss function, backpropagation, etc.)
- Understanding of Convolutional Neural Networks (CNN)
- Basic Knowledge of Computer Vision
- Understanding of Residual Learning
- Understanding of Object Segmentation

ABOUT THE SPEAKER
Aaron Gabrielle C. Dichoso is currently a 4th-year Computer Science student at De La Salle University (DLSU) under the BSMSCS Honors program. As a member of CIVI, his laboratory experiments focus on Camouflaged Crop Detection (CCD), with an interest in pursuing the improvement of CCD Network Architecture for his thesis research in the pursuit of accelerating agricultural infrastructure in the country.

Talk 2: Introduction to Single-Stage Object Detection
ABSTRACT
Single-stage object detectors have become a cornerstone in modern computer vision due to their balance of speed and accuracy. Unlike traditional two-stage detectors (e.g., Faster R-CNN) that separate region proposal and classification, single-stage detectors perform object localization and classification in one pass, making them highly efficient for real-time applications. This lecture provides a comprehensive introduction to single-stage object detection, covering the fundamental principles, popular architectures such as SSD, RetinaNet, and YOLO, as well as recent improvements in anchor-free methods. Attendees will gain insights into the design trade-offs between speed and accuracy, evaluation metrics, and practical use cases in industry and research.

The outline of the lecture is as follows:
1. Introduction to Object Detection
2. Single-Stage Object Detection Architectures
3. Two-stage vs single-stage approaches
4. Advantages and Limitations of Single-Stage Detectors
5. Applications of Single-Stage Object Detectors

Prerequisite knowledge
Attendees should be knowledgeable about these topics:
- Basic understanding of machine learning concepts
- Familiarity with convolutional neural networks (CNNs)
- Basic knowledge of object detection terminology (bounding boxes, IoU, precision/recall)

ABOUT THE SPEAKER
Matthew Ryan Carandang is an undergraduate student at De La Salle University, pursuing a degree in Computer Science as part of the honors program BSMSCS. Currently, he is conducting his thesis research on single-stage object detection, specifically developing a module to enhance a model's performance for vehicle detection.

Talk 3: Pruning in Computer Vision: Making Object Detection Lighter
ABSTRACT
As deep learning models grow increasingly complex, their deployment for real-world tasks faces challenges in computation and memory. Model optimization techniques like pruning offer practical solutions to reduce model size and improve efficiency while preserving accuracy. This lecture explores the theory of pruning, distinguishing structured and unstructured methods. While applicable to a wide range of models, this lecture highlights case studies involving YOLO object detection models, which are often optimized for mobile and edge deployment.

The outline of the lecture is as follows:
1. Motivation for optimizing object detection models
2. Overview of model compression methods
3. Pruning theory and approaches
4. Trade-offs in pruning: accuracy vs. speed vs. size

Prerequisite knowledge
Attendees should be knowledgeable about these topics:
- Basic understanding of deep learning models
- General knowledge of object detection

ABOUT THE SPEAKER
Rafael Subo A. Yap is currently a 3rd-year Computer Science student at De La Salle University (DLSU) under the BSMSCS Honors program. He is a tutor with the Peer Tutors Society (PTS) and a member of the Center for Computational Imaging & Visual Innovations (CIVI). His current research focuses on the optimization of deep learning models for counting small objects, with a particular interest in mosquito egg detection as a tool for vector surveillance and public health.

Talk 4: Curriculum Learning for Image Classification
ABSTRACT
Curriculum learning (CL) is a learning paradigm that trains machine learning models the same way that humans learn, by starting with easier problems and gradually increasing the difficulty. This strategy has shown to improve generalization and convergence rate for a variety of models in computer vision and other fields. This lecture will cover the core concepts behind CL, including a review of its history, variations, and applications.

The outline of the lecture is as follows:
1. Introduction and motivations
2. Definitions of CL
3. Theoretical basis
4. General framework
5. Potential Applications
6. Conclusion

Prerequisite knowledge
Attendees should be knowledgeable about these topics:
- Basic understanding of machine learning concepts
- Have an understanding of CNN architectures

ABOUT THE SPEAKER
Christian V. Tia is an undergraduate student in the BSMS Computer Science program at De La Salle University (DLSU). His research focuses on automated diabetic retinopathy classification and how it can be optimized in low-resource settings using smartphone-based fundus imaging.