A systematic review of 60 strawberry detection studies finds that 35 percent applied data augmentation before dataset ...

Underwater images are affected by spectral attenuation, particulate scattering, and nonuniform illumination and therefore often exhibit color shifts, reduced contrast, blurred edges, and local ...

Object detection in deep learning encompasses a range of approaches that identify and localise instances of semantic classes in images. Early methods relied on two-stage architectures, first ...

A hybrid deep learning framework combining lightweight super-resolution, transformer-based masked attention, and density ...

Autonomous vehicles require object detection systems to navigate traffic and avoid obstacles on the road. However, current detection methods often suffer from diminished detection capabilities due to ...

A comparison of the number of research papers in two types of COD, traditional and deep learning, within Image-level and Video-level applications. The bar chart shows the increase in overall research ...

Umashanker Bandari's co-authored IEEE Xplore paper explores a context-aware deep learning framework for accurate, efficient, real-time multi-object detection in traffic surveillance, addressing ...

The novel technique is based on the VarifocalNet deep-learning object detection framework, which was reportedly tweaked to achieve quicker and more accurate results. Compared to other such methods, ...