"We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-based ...

Fine-tuning a Faster R-CNN object detection model using PyTorch for improved object detection accuracy. This repository provides a Jupyter Notebook that takes you through the steps of re-training a ...

Multiple object detection is a key challenge in object detection. Feature extraction and occlusion handling are two key elements in multiple object detection. However, existing methods do not perform ...

The dataset has around 164K images based on 80 categories, also called as classes. Thus, this object detection model takes an image from the user and then with the help of YOLO algorithm, predicts the ...

Abstract: “This paper introduces a highly flexible, quantized, memory-efficient, and ultra-lightweight object detection network, called TinyissimoYOLO. It aims to enable object detection on ...

The proposed model has four main steps, namely, preprocessing, segmentation, feature extraction, and classification. Initially, the input SAR image is pre-processed using a histogram equalization ...

AI detects objects in images by using computer vision techniques that analyze the visual features of an image. The process typically involves using a convolutional neural network (CNN) to identify ...

The detection module design is based on a novel CNN implementation running on FPGA’s PL fabric, together with a tracking module using novel background-differencing technique to locate UAV-shaped ...