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MMFW-UAV dataset: multi-sensor and multi-view fixed-wing UAV dataset for air-to-air vision tasks.

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January 2025

National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology, Beijing, 100081, China.

We present an air-to-air multi-sensor and multi-view fixed-wing UAV dataset, MMFW-UAV, in this work. MMFW-UAV contains a total of 147,417 fixed-wing UAVs images captured by multiple types of sensors (zoom, wide-angle, and thermal imaging sensors), displaying the flight status of fixed-wing UAVs of different sizes, appearances, structures, and stabilized flight velocities from multiple aerial perspectives (top-down, horizontal, and bottom-up views), aiming to cover the full-range of perspectives with multi-modal image data. Quality control processes of semi-automatic annotation, manual check, and secondary refinement are performed on each image.

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Travelable area boundaries not only constrain the movement of field robots but also indicate alternative guiding routes for dynamic objects. Publicly available road boundary datasets have outlined boundaries by binary segmentation labels. However, hard post-processes have to be done to extract from detected boundaries further semantics including the shapes of the boundaries and guiding routes, which poses challenges to a real-time visual navigation system without detailed prior maps.

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Methods: Patients experiencing STEMI as their first manifestation of coronary artery disease were analyzed, as these individuals had no prior experience with heart-related chest pain.

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Purpose: This retrospective analysis evaluates baseline F-flotufolastat positron emission tomography (PET) parameters as prognostic parameters for treatment response and outcome in patients with metastatic castration-resistant prostate cancer (mCRPC) undergoing treatment with [Lu]Lu-PSMA-I&T.

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