Patchdrivenet -

The architecture typically consists of two core components: a Global Context Network and a Patch Refinement Module. First, the Global Context Network processes the entire image at a lower resolution to establish a semantic understanding of the scene. Once the regions of interest are identified, the Patch Refinement Module zooms in on specific patches of the image that require higher precision. By applying high-resolution processing only to these critical areas, PatchDriveNet effectively bypasses the computational expense of processing the entire image in high definition. This dual-stream approach allows the system to maintain the global context necessary for navigation while achieving the pixel-perfect accuracy required for safety.

is a deep learning-based image processing framework that utilizes Convolutional Neural Networks (CNNs) to process images in a patch-wise manner . Unlike traditional computer vision models that often analyze an image holistically, Patch-Driven-Net breaks images down into smaller, localized segments—or "patches"—to better capture intricate textures and local patterns. Core Methodology patchdrivenet

bridges this gap by treating the driving scene as a set of semantically meaningful patches rather than fixed square tiles. By dynamically adjusting patch boundaries based on scene content (e.g., larger patches for sky/road, smaller patches for pedestrians/traffic signs), the model allocates computation where it matters most. The architecture typically consists of two core components:

Process 4K or 8K images by breaking them into patches rather than requiring massive, specialized GPU memory. Unlike traditional computer vision models that often analyze