Abstract: This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep ...

What if the future of robotics wasn’t a single machine but an intelligent swarm, moving as one, adapting to its environment, and executing tasks with precision? Imagine a fleet of drones navigating a ...

In response to the challenges of small object detection in UAV aerial photography, such as complex backgrounds, tiny targets, dense targets, and edge deployment, the YOLOv11n model was improved.

In recent years, the exploitation of three-dimensional (3D) data in deep learning has gained momentum despite its inherent challenges. The necessity of 3D approaches arises from the limitations of two ...

Abstract: This study focuses on enhancing the accuracy and efficiency of semantic analysis systems for recognizing moving objects within video sequences. The primary aim is to improve object detection ...

In order to more accurately detect the accuracy of word-wheel water meter digits, 2000 water meter pictures were produced, and an improved Faster-RCNN algorithm for detecting water meter digits was ...

This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes and segmentation masks for each instance of an object in the image. It's based on Feature ...

Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning ...