Roboflow has launched RF-DETR, a real-time object detection model tailored for embedded systems ... split large images—like charts, tables, or diagrams—into tiles for more efficient feature ...

Abstract: Real-time object ... detection in UAV videos. StreamFlow incorporates Flow-Guided Dynamic Prediction (FGDP) to refine position predictions using local optical flow information and Optical ...

In the object detection task, directly predicting the size and center position of the bounding box may lead to too large a space of the solution, which makes it difficult for the model to converge ...

Abstract: With the prosperity of deep learning (DL) techniques, salient object detection in remote sensing images (RSI ... struggle for salient feature learning with the aid of heavy model ...

The DL model has an inherent advantage when it comes to SAR image processing jobs because of its deep feature extraction structure and strong feature extraction capability. Therefore, this research ...

This paper proposes a novel model fusion approach to enhance predictive capabilities of vision and language models by strategically integrating object detection and large language ... data remains in ...

at the time that the TensorFlow object detector is created and initialized. Note that for Freight Frenzy, the default TensorFlow inference model is optimized for a camera in landscape mode. This means ...

YOLOv7 is already being regarded as a milestone in the object detection industry ... attempt to boost model accuracy. So what are these trainable bags of freebies in YOLOv7? Let’s have a look. The ...

Object localization and classification is a difficult area of study in computer vision because of the complexity of the two processes working together. One of the most significant advances in deep ...