YOLOPears: a novel benchmark of YOLO object detectors for multi-class pear surface defect detection in quality grading systems.

Pears are one of the most widely consumed fruits, and their quality directly impacts consumer satisfaction. Surface defects, such as black spots and minor blemishes, are crucial indicators of pear quality, but it is still challenging to detect them due to the similarity in visual features. This study presents PearSurfaceDefects, a self-constructed dataset, containing 13,915 images across six categories, with 66,189 bounding box annotations.

Virus-Like Vesicles Based on Semliki Forest Virus-Containing Rabies Virus Glycoprotein Make a Safe and Efficacious Rabies Vaccine Candidate in a Mouse Model.

Rabies is a fatal zoonosis that causes encephalitis in mammals, and vaccination is the most effective method to control and eliminate rabies. Virus-like vesicles (VLVs), which are characterized as infectious, self-propagating membrane-enveloped particles composed of only Semliki Forest virus (SFV) replicase and vesicular stomatitis virus glycoprotein (VSV-G), have been proven safe and efficient as vaccine candidates. However, previous studies showed that VLVs containing rabies virus glycoprotein (RABV-G) grew at relatively low titers in cells, impeding their potential use as a rabies vaccine.

Lightweight Deep Neural Network for Joint Learning of Underwater Object Detection and Color Conversion.

Underwater image processing has been shown to exhibit significant potential for exploring underwater environments. It has been applied to a wide variety of fields, such as underwater terrain scanning and autonomous underwater vehicles (AUVs)-driven applications, such as image-based underwater object detection. However, underwater images often suffer from degeneration due to attenuation, color distortion, and noise from artificial lighting sources as well as the effects of possibly low-end optical imaging devices.