CenterNet is an innovative one-stage object detector that uses a triplet detection method instead of the traditional pair. It improves recognition accuracy by utilizing two customized modules, namely, cascade corner pooling and center pooling. These modules collect rich information from the top-left and bottom-right corners and provide more recognizable information at the central regions of an object.
How does CenterNet work?
CenterNet is an efficient object detection framework that can accur
What is CPN?
CPN, also known as the Contour Proposal Network, is a cutting-edge technology used to detect and identify objects in images. Specifically, CPN is used to identify possibly overlapping objects in an image while simultaneously creating closed object contours that are incredibly precise down to the pixel level. CPN is considered a state of the art technology in the field of object detection and is capable of effectively integrating with other object detection architectures, making a f
What is CornerNet-Saccade?
CornerNet-Saccade is an advanced version of CornerNet, which is an object detection model that can identify the corners of an object in an image. The CornerNet-Saccade model adds an attention mechanism, which operates similar to saccades in human vision, to more efficiently and effectively locate objects within an image.
How does CornerNet-Saccade work?
CornerNet-Saccade uses a multi-stage process to detect objects in an image. First, the full image is reduced in s
CornerNet-Squeeze is a cutting-edge object detector that builds on the innovation of CornerNet. By integrating a new, compact hourglass architecture that utilizes fire modules with depthwise separable convolutions, CornerNet-Squeeze can detect objects in a more streamlined and efficient manner.
What is CornerNet?
Before delving into the specifics of CornerNet-Squeeze, it’s important to understand the foundational technology it builds upon: CornerNet. Developed by the University of California,
CornerNet Overview: Object Detection Made Simple
If you've ever wondered how computers are able to recognize objects in pictures, one of the techniques used is called object detection. This involves a machine learning model that can identify where objects are located in an image by drawing a bounding box around them. One of the latest object detection models available is called CornerNet.
CornerNet takes a unique approach to object detection by detecting an object bounding box as a pair of key
EfficientDet: Revolutionizing Object Detection
Object detection is a critical task in computer vision that involves locating and classifying objects within an image. It has a wide range of applications, from self-driving cars to surveillance systems to medical imaging. One of the most powerful and efficient object detection models is EfficientDet, which has recently gained popularity due to its outstanding performance and speed.
Optimizing Object Detection
EfficientDet is an object detection
Overview of ExtremeNet
ExtremeNet is an advanced object detection framework that detects the four extreme points (top-most, left-most, bottom-most, right-most) of an object. This framework uses a keypoint estimation approach to locate extreme points by predicting multi-peak heatmaps for each object category. Additionally, ExtremeNet uses one heatmap per category to predict the object center, by calculating the average of two bounding box edges in both the x and y dimensions.
How ExtremeNet Wo
Introduction to FCOS: An Anchor-Box Free Object Detection Model
If you're someone who is interested in computer vision, you might have come across the term "object detection". Object detection is a crucial task in computer vision, where the objective is to detect objects present in an image or video. Over the past few years, many object detection models have been developed, and one such model is called FCOS.
FCOS stands for Fully Convolutional One-Stage Object Detection, and it is an anchor-bo
Introduction to FoveaBox: A Revolution in Object Detection
If you're interested in computer vision and object detection, chances are you've heard of FoveaBox. Developed by a team of researchers from Huazhong University of Science and Technology, FoveaBox is a groundbreaking method for detecting objects in images and video. Unlike traditional anchor-based methods, FoveaBox is an anchor-free approach that has been shown to be both faster and more accurate than other methods.
But what exactly is
M2Det is a sophisticated object detection model that works by extracting features from input images and producing dense bounding boxes and category scores based on learned features. The model uses a Multi-Level Feature Pyramid Network (MLFPN), which is a type of neural network that can extract features at different scales from an image, allowing it to identify objects with greater accuracy.
How M2Det Works
When an image is passed into M2Det, it is first run through the MLFPN. This network is
Overview of PP-YOLO
PP-YOLO is an object detector based on YOLOv3 that is designed to improve the accuracy of detection while maintaining the speed of the model. It aims to achieve this goal by combining various tricks that don't increase the number of model parameters and FLOPs.
What is YOLOv3 and Object Detection?
Before we dive into PP-YOLO, let's first understand what YOLOv3 and object detection are. YOLOv3 is a real-time object detection system that can recognize multiple objects in an
RetinaMask is an advanced object detection method that enhances the capabilities of the RetinaNet technique. It achieves this by including various technical advancements such as instance mask prediction, adaptive loss, and including more challenging examples during the training process.
The Concept of Object Detection
Object detection is a key objective in the field of computer vision, which is the study of how computers can be made to interpret and understand images and videos. Object detect
RetinaNet-RS is an advanced object detection model that works by scaling up the input resolution from 512 to 768 and changing the ResNet backbone depth from 50 to 152. This model is an improvement upon the original RetinaNet.
What is RetinaNet?
RetinaNet is an object detection model that uses a one-stage approach to detect objects. In contrast to traditional two-stage models, RetinaNet uses a single neural network to generate object proposals and classify objects at the same time. This approa
RetinaNet is a powerful object detection model that uses a focal loss function to address class imbalance during training. This one-stage detector is made up of a backbone network and two subnetworks that work together to detect objects in an image.
What is RetinaNet?
RetinaNet is an advanced object detection model that uses a single, unified network composed of a backbone network and two task-specific subnetworks. The backbone network is responsible for computing a convolutional feature map
Have you ever heard of RFB Net? It may sound like something out of a science fiction movie, but it's actually a type of object detector that uses a receptive field block module. This technology is used to identify objects in images or videos, and it's becoming increasingly popular in the world of computer vision.
What is RFB Net?
Simply put, RFB Net is an object detector that uses a specific type of module called a receptive field block to identify objects in an image or video. This technolog
SSD stands for single-stage object detection, a type of method used in computer vision to identify objects in images. It discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, allowing it to handle objects of various sizes.
How Does SSD Work?
At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the ob
What is YOLOP?
YOLOP is a new technology in the field of self-driving cars that stands for "You Only Look Once Perception". It is a driving perception network that performs multiple tasks simultaneously such as traffic object detection, drivable area segmentation, and lane detection. YOLOP uses a lightweight CNN to extract image features which are then fed to three decoders to complete their respective tasks. YOLOP is considered as a lightweight version of Tesla's HydraNet self-driving vehicle
YOLOv1: The Revolutionary Single-stage Object Detection Model
YOLOv1 is a groundbreaking object detection model that has greatly revolutionized object detection in computer vision. It is a single-stage object detection model that uses deep neural networks to identify objects in images, making it faster and more accurate than previous object detection methods.
How YOLOv1 Works
The YOLOv1 network transforms object detection into a regression problem. By using spatially separated bounding boxes