Dec 4, 2023 · Real-world applications/examples of GoogLeNet CNN architecture include Street View House Number (SVHN) digit recognition task, which is often used as a proxy for roadside object detection. Below is the simplified block diagram representing GoogLeNet CNN architecture:

YOLO is a Convolutional Neural Network (CNN) for performing object detection in real-time. CNNs are classifier-based systems that can process input images as structured arrays of data and identify patterns between them. Object detection is framed as. a regression problem to spatially separated bounding boxes and associated class probabilities.

Object detection is identifying object or locating the instance of interest in-group of suspected frames. Object tracking is identifying trajectory or path; object takes in the concurrent...

Oct 19, 2024 · In this article, we’ll break down the key components that power YOLOv8, starting from fundamental concepts like convolutional neural networks and residual blocks, and moving toward advanced...

Aug 29, 2021 · In the previous article Introduction to Object Detection with RCNN Family Models we saw the RCNN Family Models which gave us the way for single stage object detector. After reading this article...

Aug 1, 2021 · CNN-based Object Detectors are primarily applicable for recommendation systems. YOLO (Y ou O nly L ook O nce) models are used for Object detection with high performance. YOLO divides an...

In this article the author proposes Faster R-CNN based on deep learning method for detecting desired object from a data set.

Figure 2 shows the basic block diagram of object detection. The application of object detection covers wide areas, such as medical imaging, security, video surveillance, self-driving...

Basic block diagram of object detection and tracking is shown in Fig. 1. Data set is divided into two parts. 80 % of images in dataset are used for training and 20 % for testing. Image is considered to find objects in it by using algorithms CNN and YOLOv3. A bounding box is formed across object with Intersection over union (IoU) > 0.5.

This detection process is carried out by searching each part of an image to locate the object parts whose geometric or photometric properties match the target object in the training database....