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Bush Detection for vision-based UGV Guidance in Blueberry Orchards: Data Set and Methods

Bush Detection for Vision-based UGV Guidance in Blueberry Or...

引用

2023 ieee/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Filipović, Vladan Stefanović, Dimitrije Pajević, Nina Grbović, Željana Djuric, Nemanja Panić, Marko BioSense Institute Novi Sad Serbia

ISBN: (纸本)9798350302493

Object detection has reached strong performance in the last decade, having seen its usage spreading to various application areas, such as medicine, transportation, sports, and others. However, one of the more underutilized areas where advanced detection methods have yet to fully fulfill their promise is in the area of agriculture, where a strong potential exists for applying learned models to achieve practical, real-world impact affecting a large number of people. In this work, we focus on this application area and consider the problem of orchard guidance for ground robots, focusing on obstacle and plant detection from RGB camera images. First, we present an overview of public data sets used to train models to detect relevant objects from camera images and other sensor inputs. Then we introduce a novel data set collected in blueberry orchards that contains camera images in various conditions and provides blueberry bushes as targets for detection. The introduced data set provides the research community with a novel task of blueberry bush detection, which was not commonly considered thus far due to the lack of relevant data sets. We describe a detailed analysis of the data set, and finally provide an experimental study with several state-of-the-art deep object detection models, that set a baseline for the performance on this novel data set. The data set is made available online, enriching the variability of the existing tasks in the field and supporting further development of smart agriculture applications. © 2023 ieee.

关键词： Object detection

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CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

CLIP-Art: Contrastive Pre-training for Fine-Grained Art Clas...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Conde, Marcos, V Turgutlu, Kerem Univ Valladolid Valladolid Spain Adobe Mountain View CA USA

ISBN: (纸本)9781665448994

Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation. In this work, we use CLIP (Contrastive Language-Image Pre-Training) [12] for training a neural network on a variety of art images and text pairs, being able to learn directly from raw descriptions about images, or if available, curated labels. Model's zero-shot capability allows predicting the most relevant natural language description for a given image, without directly optimizing for the task. Our approach aims to solve 2 challenges: instance retrieval and fine-grained artwork attribute recognition. We use the iMet Dataset [20], which we consider the largest annotated artwork dataset. Our code and models will be available at https://***/KeremTurgutlu/clip_art

关键词： Training computer vision Art Image recognition Databases Natural languages Neural networks

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Smart Infant Monitoring System Using computer vision and AI

Smart Infant Monitoring System Using Computer Vision and AI

引用

2023 ieee International conference on Electro Information Technology, eIT 2023

作者： Singh, Gurpreet Shekhar, Abhishek Raj Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

The new era of technology is being greatly influenced by the field of artificial intelligence. computer vision and deep learning have become increasingly important due to their ability to process vast amounts of data and provide insights and solutions in a variety of fields. computer vision, deep learning and signal analysis have been used in a growing number of applications and services including smart devices, image, and speech recognition, healthcare, etc., one such device is an infant monitoring system. It monitors the daily activities of the infant such as their sleeping patterns, sounds, and movements. In this paper, deep learning and computer vision libraries were used to develop algorithms to detect whether the infant was in any uncomfortable situation such as sleeping on its back, face being covered and whether the infant was awake. The smart infant monitoring system detects the infant's unsafe resting situation in real time and sent immediate alerts to the caretaker's device. This paper presents the design flow of a smart infant monitoring system consisting of a night vision camera, a Jetson Nano, and a Wi-Fi internet connection. The pose estimation and awake detection algorithms were developed and tested successfully for different infant resting/sleeping situations. The smart infant monitoring system provides significant benefits for safety and an improved understanding of infants' sleep patterns and behavior. © 2023 ieee.

关键词： Face recognition

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Study of Enhanced Communication of Human-computer Using Gesture recognition Technique 10

Study of Enhanced Communication of Human-Computer Using Gest...

引用

10th International conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Rastogi, Rupali Arya, Ajay Chaudhary, Aman Chola, Abhinav Josan, Pushpinder Kaur Central Research Laboratory Bharat Electronics Limited Ghaziabad India

ISBN: (纸本)9798350384369

Hand gestures serve as a fundamental mode of non- verbal communication, intricately conveying messages through the positioning of palms, arrangement of fingers, and the form of the hand itself. Their significance lies in their ability to facilitate intuitive communication and provide a natural avenue for interaction. Gesture recognition, categorized into sensor-based and vision-based approaches, forms the backbone of systems enabling seamless human-machine communication. Sensor-based systems employ wearable devices such as data gloves and wrist bands, while vision-based systems utilize cameras or depth sensors like Leap Motion Controller (LMC) and Microsoft Kinect. This paper delves into the realm of depth- sensing techniques within gesture recognition, aiming to present a comprehensive analysis and comparison. By exploring existing methodologies, this study seeks to shed light on the advancements, challenges, and future prospects in the dynamic field of hand gesture recognition. © 2024 ieee.

关键词： Gesture recognition

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A vision-Based recognition and Navigation Method for Autonomous Underwater Robot Grasping

A Vision-Based Recognition and Navigation Method for Autonom...

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2024 ieee International conference on Robotics and Biomimetics, ROBIO 2024

作者： Luo, Yudong Xie, Feiyu Wang, Tong Zhao, Na Shen, Yantao Dalian Maritime University Department of Computer Science and Technology Liaoning Dalian116026 China University of Nevada Department of Electrical and Biomedical Engineering RenoNV89557 United States

ISBN: (纸本)9781665481090

The development of underwater robotic systems with autonomous grasping capabilities is challenging due to the complexity of the operation environment, limited sensing performance, and computation load. This paper proposes a fast recognition and navigation method for underwater robot grasping. In detail, we theoretically demonstrate the feasibility of underwater robots that can quickly identify and localize targets at a long distance through a monocular camera, thus navigating the robot to approach the target in real-time and then generate accurate grasping points at short-range through stereo-vision cameras to help the robot complete autonomous grasping. Experiment results show that our constructed system can recognize and determine the target's position within 0.03 seconds, then detect and generate an accurate grasping point within 1 second at a distance of 50 cm from the target with the error of 23 cm. Our research proves that underwater robots equipped with low-cost devices such as commercial stereo-vision cameras and edge computing devices can identify targets and generate accurate grasping points in real-time, promoting research in the field of autonomous underwater grasping. © 2024 ieee.

关键词： Stereo vision

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Machine vision based sorting method for precision industrial components

Machine vision based sorting method for precision industrial...

引用

International conference on computer vision, Robotics, and Automation Engineering, CRAE 2024

作者： Li, Hongming Ding, Ye Shao, Mingyu Dongguan University of Technology Dongguan523808 China

ISBN: (纸本)9781510682283

With the rapid development of industrial automation, automated sorting of precision industrial components has become the key to improve production efficiency and reduce costs. In this paper, a machine vision-based sorting method for precision industrial components is proposed, which can effectively recognize and classify iron sheets of different shapes and sizes. This study innovatively integrates image processing, pattern recognition and machine vision technologies to design an efficient automated sorting process. Designed for industrial precision components, this method integrates image preprocessing techniques optimized for industrial-grade image acquisition environments, accurate feature extraction methods for efficient identification of key component attributes, and advanced contour analysis and customized shape matching algorithms to ensure fast and accurate classification of all types of industrial components in automated sorting *** experimental results show that the proposed algorithm has significant advantages in improving the sorting accuracy and efficiency, and provides a new gripping and sorting technical solution for the industrial automation field. © 2024 SPIE.

关键词： Machine vision

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Focusing on what to Decode and what to Train: SOV Decoding with Specific Target Guided DeNoising and vision Language Advisor

Focusing on what to Decode and what to Train: SOV Decoding w...

引用

2025 ieee/CVF Winter conference on Applications of computer vision, WACV 2025

作者： Chen, Junwen Wang, Yingcheng Yanai, Keiji The University of Electro-Communications Department of Informatics Tokyo Japan

ISBN: (纸本)9798331510831

Recent transformer-based methods achieve notable gains in the Human-object Interaction Detection (HOID) task by leveraging the detection of DETR and the prior knowledge of vision-Language Model (VLM). However, these methods suffer from extended training times and complex optimization due to the entanglement of object detection and HOI recognition during the decoding process. Especially, the query embeddings used to predict both labels and boxes suffer from ambiguous representations, and the gap between the prediction of HOI labels and verb labels is not considered. To address these challenges, we introduce SOV-STG-VLA with three key components: Subject-Object-Verb (SOV) decoding, Specific Target Guided (STG) denoising, and a vision-Language Advisor (VLA). Our SOV decoders disentangle object detection and verb recognition with a novel interaction region representation. The STG denoising strategy learns label embeddings with ground-truth information to guide the training and inference. Our SOV-STG achieves a fast convergence speed and high accuracy and builds a foundation for the VLA to incorporate the prior knowledge of the VLM. We introduce a vision advisor decoder to fuse both the interaction region information and the VLM's vision knowledge and a Verb-HOI prediction bridge to promote interaction representation learning. Our VLA notably improves our SOV-STG and achieves SOTA performance with one-sixth of training epochs compared to recent SOTA. Code and models are available at https://***/cjw2021/SOV-STG-VLA. © 2025 ieee.

关键词： Decoding

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A Sliced Wasserstein Loss for Neural Texture Synthesis

A Sliced Wasserstein Loss for Neural Texture Synthesis

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Heitz, Eric Vanhoey, Kenneth Chambon, Thomas Belcour, Laurent Unity Technol Grenoble France

ISBN: (纸本)9781665445092

We address the problem of computing a textural loss based on the statistics extracted from the feature activations of a convolutional neural network optimized for object recognition (e.g. VGG-19). The underlying mathematical problem is the measure of the distance between two distributions in feature space. The Gram-matrix loss is the ubiquitous approximation for this problem but it is subject to several shortcomings. Our goal is to promote the Sliced Wasserstein Distance as a replacement for it. It is theoretically proven, practical, simple to implement, and achieves results that are visually superior for texture synthesis by optimization or training generative neural networks.

关键词： Training Neural networks computer architecture Production Tools Feature extraction Complexity theory

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STP-Net: Spatio-Temporal Polarization Network for action recognition using polarimetric videos

STP-Net: Spatio-Temporal Polarization Network for action rec...

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22nd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Kanth, R. Krishna Ramaswamy, Akshaya Kumar, A. Anil Gubbi, Jayavardhana Balamuralidhar, P. TCS Res Bangalore Karnataka India TCS Res Chennai Tamil Nadu India

ISBN: (纸本)9781665458245

Deep learning has brought tremendous progress in computer vision and natural language processing, and is used in multiple non-critical applications. A major bottleneck for its use in many other areas is the black box nature of these algorithms, resulting in a lack of explainability in their decisions. One of the key problems identified is the confounding effect, which causes confusion between the desired causes and other irrelevant factors affecting an outcome. This is more pronounced in the spatio-temporal case, such as the bias on the static background in the classification of a video. A way to handle this is by making use of sensors that capture additional scene properties, to mitigate spurious associations. In this work, we integrate the polarimetric videos with deep learning and evaluate it on the popular action recognition problem. We construct a dataset of polarimetric videos for fine-grained actions and study the effect of various parameters, extracted from the polarimetric video frames, as inputs to a deep network. Using these observations, we design a spatio-temporal polarization network (STP-Net) to effectively extract polarimetric features. This is evaluated on the recent HumanAct12 dataset for human activity recognition. Extensive evaluation clearly shows that the polarimetric modality is able to localize the correct action regions, leading to better generalizability.

关键词： Deep learning computer vision conferences Activity recognition Feature extraction Natural language processing Sensors

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TraDance: A dataset for assessing the performance of Greek folk dances recognition models

TraDance: A dataset for assessing the performance of Greek f...

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2023 ieee International conference on Imaging Systems and Techniques, IST 2023

作者： Balafoutis, Thanasis Balaska, Vasiliki Gasteratos, Antonios Democritus University of Thrace Department of Production and Management Engineering Xanthi Greece

ISBN: (纸本)9798350330830

In computer vision, datasets and benchmarks are widely used to compare algorithms and boost scientific progress. Especially in the human action recognition research field, extracting dance poses from video sequences for fragmentation and recognition of dancer movements is a challenging task, and new datasets are always important. This paper presents a new benchmark dataset and evaluation methodology for dancing analysis and step movements classification. We focus on traditional Greek dances, which are characterized by the repetition of simple steps throughout the duration of the dance track. The dataset, named TraDance, contains 1.4 hours of 3D dance motion videos from 5 different experienced dancers and 3494 annotated dance steps, covering 5 traditional dance genres with known RGB-D camera settings. In addition, to evaluate the quality of TraDance, we provide extensive experiments using existing machine learning algorithms that are commonly used in computer vision. The results show that TraDance is feasible for research-purpose studies and reliable. The dataset can be found online at: https://***/tradance. © 2023 ieee.

关键词： computer vision

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