Object detection ≈ Object detection
View article: Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition Open
Actualmente diversas investigaciones se han enfocado en analizar a partir de videos de alta velocidad, características de las descargas eléctricas atmosféricas con el fin de adquirir mejor comprensión del fenómeno, que conlleva entre otros…
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MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Open
We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks…
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Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning Open
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchica…
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Simple online and realtime tracking Open
This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, detection quality is identified as a key factor influencing tr…
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R-FCN: Object Detection via Region-based Fully Convolutional Networks Open
We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our re…
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You Only Look Once: Unified, Real-Time Object Detection Open
We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associate…
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Res2Net: A New Multi-Scale Backbone Architecture Open
Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to con…
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SECOND: Sparsely Embedded Convolutional Detection Open
LiDAR-based or RGB-D-based object detection is used in numerous applications, ranging from autonomous driving to robot vision. Voxel-based 3D convolutional networks have been used for some time to enhance the retention of information when …
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DOTA: A Large-Scale Dataset for Object Detection in Aerial Images Open
Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of …
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Deep Learning for Generic Object Detection: A Survey Open
Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful…
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MobileNetV2: Inverted Residuals and Linear Bottlenecks Open
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe effi…
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A Review of Yolo Algorithm Developments Open
Object detection techniques are the foundation for the artificial intelligence field. This research paper gives a brief overview of the You Only Look Once (YOLO) algorithm and its subsequent advanced versions. Through the analysis, we reac…
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RU-AI: A Large Multimodal Dataset for Machine Generated Content Detection Open
This repository contains all the collected and aligned data for RU-AI dataset. It is constructed based on three large publicly available datasets: Flickr8K, COCO, and Places205, by adding their corresponding machine-generated pairs from fi…
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A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS Open
YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO’s evolution, examining the innovations and contributions in each iter…
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PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes Open
Estimating the 6D pose of known objects is important for robots to interact with the real world.The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects.…
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YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications Open
For years, the YOLO series has been the de facto industry-level standard for efficient object detection. The YOLO community has prospered overwhelmingly to enrich its use in a multitude of hardware platforms and abundant scenarios. In this…
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DSSD : Deconvolutional Single Shot Detector Open
The main contribution of this paper is an approach for introducing additional context into state-of-the-art general object detection. To achieve this we first combine a state-of-the-art classifier (Residual-101[14]) with a fast detection f…
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Mask R-CNN Open
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. Th…
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The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale Open
We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adap…
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Focal Loss for Dense Object Detection Open
The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a reg…
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Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges Open
Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radar…
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A Survey of Deep Learning-Based Object Detection Open
Object detection is one of the most important and challenging branches of\ncomputer vision, which has been widely applied in peoples life, such as\nmonitoring security, autonomous driving and so on, with the purpose of locating\ninstances …
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DeepFruits: A Fruit Detection System Using Deep Neural Networks Open
This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic pla…
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R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object Open
Rotation detection is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. Though considerable progress has been made, for practical settings, there still exist…
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Real-Time Seamless Single Shot 6D Object Pose Prediction Open
We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot techniqu…
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YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection Open
Since its inception in 2015, the YOLO (You Only Look Once) variant of object detectors has rapidly grown, with the latest release of YOLO-v8 in January 2023. YOLO variants are underpinned by the principle of real-time and high-classificati…
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TextBoxes++: A Single-Shot Oriented Scene Text Detector Open
Scene text detection is an important step of scene text recognition system and also a challenging problem. Different from general object detection, the main challenges of scene text detection lie on arbitrary orientations, small sizes, and…
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Gliding Vertex on the Horizontal Bounding Box for Multi-Oriented Object Detection Open
Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts. In this pap…
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M2Det: A Single-Shot Object Detector Based on Multi-Level Feature Pyramid Network Open
Feature pyramids are widely exploited by both the state-of-the-art one-stage object detectors (e.g., DSSD, RetinaNet, RefineDet) and the two-stage object detectors (e.g., Mask RCNN, DetNet) to alleviate the problem arising from scale varia…
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Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification Open
Robust low-level image features have been proven to be effective representations for a variety of visual recognition tasks such as object recognition and scene classification; but pixels, or even local image patches, carry little semantic …