Patchdrivenet

Detecting small boats in a vast ocean. Global context identifies the water-sky boundary; the Patch Drive focuses on whitecaps and wake trails. False positives from wave noise reduced by 60%.

A Patch-Driven Network is a type of neural network that focuses on processing images in a patch-based manner. Unlike traditional convolutional neural networks (CNNs) that process entire images at once, PDNs divide the input image into smaller patches and process each patch independently. This approach allows the network to capture local patterns and features within the image, which can be particularly useful for tasks such as image denoising, deblurring, and super-resolution. patchdrivenet

Patch-Driven-Net: A Deep Learning Approach for Localized Visual Processing Detecting small boats in a vast ocean

The Patch-Driven Network approach offers several advantages over traditional CNNs: A Patch-Driven Network is a type of neural

: Researchers have found that while a normal DriveNet model focuses on curbs and lane lines to steer, an adversarial patch can distract it .