Sangyun Lee

Tiny object detection (TOD) plays a vital role in robotics and control systems, particularly in applications such as dronebased surveillance, autonomous navigation, and intelligent monitoring. Detecting extremely small objects in aerial imagery poses significant challenges due to limited spatial detail and insufficient contextual information. While feature pyramid networks (FPNs) have proven effective in handling multi-scale features, conventional FPN-based approaches often suffer from reduced spatial precision and semantic richness, factors critical for reliable tiny object detection. To address these limitations, we propose an enhanced FPN architecture that integrates deformable convolution (DeformConv) and vision state space modules (VSSM). DeformConv replaces standard fixed-kernel convolutions in the lateral connections, enabling adaptive focus on sparse spatial locations to better capture geometric variations and fine-grained spatial features. In parallel, VSSM modules are incorporated into the top-down pathway to model long-range contextual dependencies, thereby mitigating the semantic information loss typically encountered in traditional FPNs. Experiments conducted on the VisDrone2019 and AI-TOD v2 datasets demonstrate that the proposed architecture consistently outperforms conventional FPN-based detectors, validating the robustness and effectiveness of our feature refinement strategy for tiny object detection.

This study proposes a campus tour mobile application called DeepCampus that leverages deep learning-based vision technologies to enhance user engagement and interaction. The proposed application introduces two core features: a photo mission, where users take photos at designated campus locations, and a commemorative photo creation function that transforms these photos into character-based styles. The photo mission relies on a CNN-based place recognition model, trained on a custom-built campus dataset, to verify mission completion. The commemorative photo creation function uses a diffusion model to generate personalized, stylized photos, providing users with unique records of their campus experience. By integrating place recognition and generative models, the application encourages active exploration. The effectiveness of the proposed system is validated through experiments on the custom dataset, demonstrating its technical reliability and potential to enhance campus tours.

In this study, we present the Discriminative Temporal Shift Module (D-TSM), an enhancement of the Temporal Shift Module (TSM) for action recognition. TSM has limitations in capturing intricate temporal dynamics due to its simplistic feature shifting. D-TSM addresses this by introducing a subtraction operation before the shifting. This enables the extraction of discriminative features between adjacent frames, thereby allowing for effective action recognition where subtle motions serve as crucial cues. It preserves TSM’s foundational philosophy, prioritizing minimal computational overhead and no additional parameters. Our experiments demonstrate that D-TSM significantly improves performance of TSM and outperforms other leading 2D CNN-based methods.

When multiple object tracking (MOT) based on the tracking-by-detection paradigm is implemented, the similarity metric between the current detections and existing tracks plays an essential role. Most of the MOT schemes based on a deep neural network learn the similarity metric using a Siamese architecture, but the plain Siamese architecture might not be enough owing to its structural simplicity and lack of motion information. This paper aims to propose a new MOT scheme to overcome the existing problems in the conventional MOTs. Feature pyramid Siamese network (FPSN) is proposed to address the structural simplicity. The FPSN is inspired by a feature pyramid network (FPN) and it extends the Siamese network by applying FPN to the plain Siamese architecture and by developing a new multi-level discriminative feature. A spatiotemporal motion feature is added to the FPSN to overcome the lack of motion information and to enhance the performance in MOT. Thus, FPSN-MOT considers not only the appearance feature but also motion information. Finally, FPSN-MOT is applied to the public MOT challenge benchmark problems and its performance is compared to that of the other state-of-the-art MOT methods.