[CIVI Academic Lecture] Small Object Detection: Tiny Targets, Big Problems
ABSTRACT
Small object detection is a specialized challenge in computer vision where objects occupy only a tiny portion of an image, making them difficult to detect and classify. This lecture explores how small and densely packed objects are defined and categorized, the visual and technical limitations of detecting them, and why many conventional detection approaches fall short. It also introduces state-of-the-art solutions, highlighting their impact on real-world tasks.
The outline of the lecture is as follows:
1. Background of Small Object Detection
2. Challenges in Detecting Small Objects
3. Techniques and Modern Solutions
4. Applications
Prerequisite knowledge
Attendees should be knowledgeable about these topics:
Basic understanding of deep learning models
Familiarity with object detection concepts like bounding boxes and anchor boxes
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.