Selected work

Representative Papers

Can Vision Models Read the Radar Display? On the Feasibility of Radar Imagery for Air Traffic Complexity Estimation

Aug 12, 2026

This study investigates whether radar imagery can serve as an effective input modality for deep vision models to estimate air traffic complexity. The traffic situation is encoded into a five-channel image incorporating positional, heading, speed, and altitude information. For the first time, a Vision Transformer is employed to perform regression on four intrinsic complexity components derived from geometric relationships among aircraft. Experimental results demonstrate that the proposed approach achieves R² values exceeding 0.96 across all complexity components. Perturbation analysis further reveals the model’s sensitivity to key aircraft, with its responses closely aligning with those aircraft’s actual contributions to overall complexity, thereby validating the efficacy of sparse, self-similar radar images for modeling air traffic complexity.

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LiM-YOLO: Less is More with Pyramid Level Shift and Normalized Auxiliary Branch for Ship Detection in Optical Remote Sensing Imagery

Dec 10, 2025

Optical remote sensing images pose significant challenges for ship detection due to extremely small object scales and strong anisotropic shapes, leading to high miss rates and localization inaccuracies in general-purpose detectors. To address this, we propose a lightweight and efficient YOLO architecture specifically tailored for remote sensing ship detection. Our key contributions are: (1) a novel pyramid-level offset strategy that relocates detection heads to feature levels P2–P4, satisfying the Nyquist sampling criterion to preserve fine spatial details of slender ships; (2) a group-normalized linear projection convolutional block (GN-CBLinear) that enhances stability in high-resolution feature extraction and robustness under small-batch training; and (3) integration of remote sensing–specific data augmentation and post-processing techniques. Extensive experiments on SODA-A, DOTA-v1.5, FAIR1M-v2.0, and ShipRSImageNet-V1 demonstrate state-of-the-art performance—achieving higher accuracy, fewer parameters, and faster inference compared to existing methods.

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Recent publications

Latest Papers

Can Vision Models Read the Radar Display? On the Feasibility of Radar Imagery for Air Traffic Complexity Estimation

Aug 12, 2026

This study investigates whether radar imagery can serve as an effective input modality for deep vision models to estimate air traffic complexity. The traffic situation is encoded into a five-channel image incorporating positional, heading, speed, and altitude information. For the first time, a Vision Transformer is employed to perform regression on four intrinsic complexity components derived from geometric relationships among aircraft. Experimental results demonstrate that the proposed approach achieves R² values exceeding 0.96 across all complexity components. Perturbation analysis further reveals the model’s sensitivity to key aircraft, with its responses closely aligning with those aircraft’s actual contributions to overall complexity, thereby validating the efficacy of sparse, self-similar radar images for modeling air traffic complexity.

0 citationsRead paper

LiM-YOLO: Less is More with Pyramid Level Shift and Normalized Auxiliary Branch for Ship Detection in Optical Remote Sensing Imagery

Dec 10, 2025

Optical remote sensing images pose significant challenges for ship detection due to extremely small object scales and strong anisotropic shapes, leading to high miss rates and localization inaccuracies in general-purpose detectors. To address this, we propose a lightweight and efficient YOLO architecture specifically tailored for remote sensing ship detection. Our key contributions are: (1) a novel pyramid-level offset strategy that relocates detection heads to feature levels P2–P4, satisfying the Nyquist sampling criterion to preserve fine spatial details of slender ships; (2) a group-normalized linear projection convolutional block (GN-CBLinear) that enhances stability in high-resolution feature extraction and robustness under small-batch training; and (3) integration of remote sensing–specific data augmentation and post-processing techniques. Extensive experiments on SODA-A, DOTA-v1.5, FAIR1M-v2.0, and ShipRSImageNet-V1 demonstrate state-of-the-art performance—achieving higher accuracy, fewer parameters, and faster inference compared to existing methods.

0 citationsRead paper