This paper introduces a novel benchmark for efficient upscaling as part of the NTIRE 2023
Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images …

The well-designed structures in neural networks reflect the prior knowledge incorporated
into the models. However, though different models have various priors, we are used to …

There is a great demand for dragon fruit in China and Southeast Asia. Manual picking of
dragon fruit requires a lot of labor. It is imperative to study the dragon fruit-picking robot. The …

Violence is a serious threat to societal health; preventing violence in airports, airplanes, and
spacecraft is crucial. This study proposes the Violence-YOLO model to detect violence …

Real-time object detection plays a crucial role in edge devices applications. Pruning
methods are usually used to effectively eliminate redundant parameters of the object …

The skin, the body's largest organ, plays a critical role in protection and regulation, making
its health essential. Skin cancer, one of the most prevalent malignancies, continues to rise …

Traditional manual visual detection methods are inefficient, subjective, and costly, making
them prone to false and missed detections. Deep-learning-based defect detection identifies …

Recently, deep learning methods have widely been employed for surface defect
segmentation in industrial production with remarkable success. Nevertheless, accurate …

Interest in anti-UAV detection systems has increased due to growing concerns about the
security and privacy issues associated with unmanned aerial vehicles (UAVs). Achieving …

Single image super-resolution (SR) is an important low-level vision task, and the dynamic
SR trading off performance and efficiency are increasingly in demand. The existing dynamic …