The University of Osaka · Computer Science
Professor Yihong Zhang's research lab specializes in advanced sensing and detection technologies, with a focus on lightweight and real-time object detection for unmanned aerial vehicles (UAVs), particularly in challenging environmental conditions such as fog, low light, and glare. The lab also investigates biomedical modeling, including fractional-order dynamics of viral co-infections and immune responses, and develops innovative optical systems for sub-diffraction-limited beam shaping using nonlinear crystals. Their work bridges computational intelligence, biomedical engineering, and photonics, with applications in autonomous systems, public health, and precision imaging.
Figures are computed from collected data and may differ slightly.
The cytokines interleukin-1 beta (IL-1 beta) and tumor necrosis factor alpha (TNF-alpha) are released by mononuclear phagocytes in vitro after stimulation with mycobacteria and are considered to mediate pathophysiologic events, including granuloma formation and systemic symptoms. We demonstrated that the Mycobacterium tuberculosis cell wall component lipoarabinomannan (LAM) is a very potent inducer of IL-1 beta gene expression in human monocytes and investigated the mechanism of this effect. We
For a traveler to enjoy a trip in a city, one important factor is the diversity of sceneries and facilities along the route. Current navigation systems can provide the shortest route between two points, as well as scenic or safe routes. However, diversity is largely ignored in existing works. In this paper, we present a system that provides diversity-based route recommendation. It measures visual-based diversity and facility-based diversity with information extracted from publicly available data
Deploying target detection models on edge devices such as UAVs is challenging due to their limited size and computational capacity, while target detection models typically require significant computational resources. To address this issue, this study proposes a lightweight real-time infrared object detection model named LRI-YOLO (Lightweight Real-time Infrared YOLO), which is based on YOLOv8n. The model improves the C2f module’s Bottleneck structure by integrating Partial Convolution (PConv) wit
<abstract><p>In this paper, a fractional order HIV/HTLV co-infection model with HIV-specific antibody immune response is established. Two cases are considered: constant control and optimal control. For the constant control system, the existence and uniqueness of the positive solutions are proved, and then the sufficient conditions for the existence and stability of five equilibriums are obtained. For the second case, the Pontryagin's Maximum Principle is used to analyze the optimal c
With the rapid advancement of UAV technology, robust object detection under adverse weather conditions has become critical for enhancing UAVs’ environmental perception. However, object detection in such challenging conditions remains a significant hurdle, and standardized evaluation benchmarks are still lacking. To bridge this gap, we introduce the Adverse Weather Object Detection (AWOD) dataset—a large-scale dataset tailored for object detection in complex maritime environments. The AWOD datase
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