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Deep learning has gained considerable interest in agricultural breeding research. While advances in sequencing technologies have made genotypic data collection easier in genomic breeding, phenotypic data collection remains labor intensive and time consuming. Furthermore, as traditional phenotypic data collection relies heavily on manual processes, the results may vary based on the researcher’s skill and criteria. Thus, automated phenotypic data collection is essential for addressing these challenges. In this study, we aimed to develop a deep learning model using the YOLOv8 framework to measure the lengths of hypocotyls and roots in sprout vegetables such as mung bean, cowpea, and soybean. Our model automates the measurement process, accurately identifies the hypocotyl and root using Roboflow, and subsequently measures their lengths with high precision in various legume species. This approach addresses the challenges of extensive phenotypic data collection, which is essential for genetic breeding and agricultural improvement. Our deep learning model facilitates consistent and accurate data collection in large-scale studies by controlling variables influenced by the researcher’s skills and criteria. This reduces errors and enhances data reliability and accuracy, which are crucial for successful breeding practices and agricultural research. [ABSTRACT FROM AUTHOR]

This paper proposes a deep learning-based automatic access management system for nursing rooms using maternity authentication. The system aims to enhance the convenience and safety of pregnant women and caregivers accompanied by infants. It verifies pregnant women through a smartphone application and is designed to automatically open the nursing room door after detecting whether they are accompanied by an infant using object recognition and posture detection technologies. The deep learning-based object recognition and posture detection algorithms demonstrated high accuracy in real-world tests, ensuring safe access for both pregnant women and caregivers with infants. This system has been evaluated to significantly improve convenience and security compared to existing nursing room access management methods. Moreover, positive user feedback has confirmed the system's effectiveness and necessity. This system can be applied to public transportation and public facilities, enhancing accessibility for pregnant women and caregivers, while contributing to the spread of a culture of social consideration. [ABSTRACT FROM AUTHOR]

In this study, we aim to validate the use of Artificial Intelligence of Things (AIoT) for deep learning-based detection of piglet crushing incidents by sows in pig farms, a leading cause of piglet mortality. To achieve this, we developed a deep neural network based on the You Only Look Once (YOLO) model, which was trained to detect piglet crushing events in real-time. We then optimized the weights of the YOLO model using TensorFlow Lite, TensorRT, and edge TPU to ensure efficient operation on AIoT devices with limited computational resources. Finally, we compared the performance of the deep learning-based piglet crushing detection algorithm on various single-board computers (SBCs), including the Jetson Nano, Raspberry Pi, and Coral Dev Board, to validate its suitability for real-world applications. [ABSTRACT FROM AUTHOR]

In Korea, feasibility analysis is conducted to build facilities. Currently, basic data for analyzing the candidate sites of the facility or the lack of land is processed using manpower and final facility permission is carried out using the results derived from algorithm analysis by experts. If GIS data of expected facilities is automated from collection to analysis to analyze the validity of facility construction by accessing mobile or the web, decision-making will be made quickly. The goal of this study is to conduct facility permits online by analyzing Big Data on mobile or web for facility construction and deriving optimal points. It explores the applicability of YOLO and Fast R-CNN, object detection deep learning algorithms, and suggests ways to provide user-friendly optimal point analysis results in web and mobile environments for optimal point analysis using big data analysis information and spatial data such as aerial images. [ABSTRACT FROM AUTHOR]

In modern factory automation, various cutting-edge technologies such as mechanical, electrical, electronic, and computer engineering are applied in the manufacturing process to increase production efficiency and improve product quality. In this study, a YOLO-based cigarette detection system using a single circular bounding box is proposed to accurately detect defective packaging, such as missing cigarettes or reverse alignment in the cigarette production process. In particular, Proposed-Net2 shows an average precision similar to that of the YOLOv4-Tiny network in terms of performance, uses 10% of memory compared to the existing ones, and detects cigarettes with about twice as fast processing time. And it was confirmed that effective learning is possible by augmenting various types of data with only a small number of training images by combining object-based cropped images with rotation and randomly generated bounding boxes for insufficient training data. This is applicable even when hardware specifications are low, such as an embedded environment. [ABSTRACT FROM AUTHOR]

This study proposes an effective method for detecting multiple targets in frequency-modulated interrupted continuous wave (FMICW) radar by using periodic signal characteristics and applying machine learning. FMICW radars are typically used for detecting a single target, but this method overcomes the challenges of range-Doppler coupling and spurious wave generation caused by the switching of transmit and receive signals, to detect multiple targets. The method first uses FMICW radar to generate an RD-Map by applying MLE (maximum likelihood estimation), and then enhances the targets by identifying the intersection of lines generated by range-Doppler coupling. Thus, by applying a machine-learning-based object detection algorithm to the enhanced images, multiple target detection be- comes possible. [ABSTRACT FROM AUTHOR]

Recently, AI, big data, and IoT technologies are being used in various solutions such as fire detection and gas or dangerous substance detection for safety accident prevention. According to the status of occupational accidents published by the Ministry of Employment and Labor in 2021, the accident rate, the number of injured, and the number of deaths have increased compared to 2020. In this paper, referring to the dataset construction guidelines provided by the National Intelligence Service Agency(NIA), the dataset is directly collected from the field and learned with YOLOv4 to propose a collision risk object detection system through object detection. The accuracy of the dangerous situation rule violation was 88% indoors and 92% outdoors. Through this system, it is thought that it will be possible to analyze safety accidents that occur in industrial sites in advance and use them to intelligent platforms research. [ABSTRACT FROM AUTHOR]

AI-based image processing technologies in various fields have been widely studied. However, the lighter the board, the more difficult it is to reduce the weight of image processing algorithm due to a lot of computation. In this paper, we propose a method using deep learning for object recognition algorithm in lightweight embedded boards. We can determine the area using a deep neural network architecture algorithm that processes semantic segmentation with a relatively small amount of computation. After masking the area, by using more accurate deep learning algorithm we could operate object detection with improved accuracy for efficient neural network (ENet) and You Only Look Once (YOLO) toward executing object recognition in real time for lightweighted embedded boards. This research is expected to be used for autonomous driving applications, which have to be much lighter and cheaper than the existing approaches used for object recognition. [ABSTRACT FROM AUTHOR]

This study investigates the underlying mechanism of YOLO in millennial consumer, especially the influence of perceived economic recession on the present-biased preference. In addition, it was attempted to expand the implicit theory by proposing the individual’s entity belief as a mediator for the effect of perceived economic situation on consumers’ present-biased seeking behavior. In three experimental studies, undergraduate students who both highly primed and measured negative economic situation showed more favorable attitudes towards present-biased persuasive message and related products. The results of this research provides practical implication for marketers especially in the current situation experiencing economic slowdown due to low economic growth and COVID 19. [ABSTRACT FROM AUTHOR]

In modern society, deaths and property damage due to drowsiness occur every year enormously. Methods to reduce such damage are being studied a lot in all walks of life, and research on preventing drowsy driving is particularly active in automobiles. In this paper, as an Arduino-based water gun firing system that learns open and closed eyes using YOLO, we propose a drowsy prevention system that fires a water gun when the duration of the closed eye exceeds a certain time. This system can be applied and used in various fields, but especially when applied to a car, it is not necessary to purchase expensive specifications and if you pay a little attention, you can reduce accidents caused by drowsy driving by 100% at a very low cost. In addition, it can be said that it is an independent system that overcomes different specifications for each company. [ABSTRACT FROM AUTHOR]

Recently, the “Act of Serious Disasters, etc” was enacted and institutional and social interest in safety accidents is increasing. In this paper, we analyze statistical data published by government agency on safety accidents that occur in manufacturing sites, and compare various object detection models based on deep learning to build a model to determine dangerous situations to reduce the occurrence of safety accidents. The data-set was directly constructed by collecting images from CCTVs at the manufacturing site, and the YOLO-v4, SSD, CenterNet models were used as training data and evaluation data for learning. As a result, the YOLO-v4 model obtained a value of 81% of mAP. It is meaningful to select a class in an industrial field and directly build a dataset to learn a model, and it is thought that it can be used as an initial research data for a system that determines a risk situation and infers it. [ABSTRACT FROM AUTHOR]

Automobiles have been used as simple means of transportation, but recently, as automobiles are rapidly becoming intelligent and smart, and automobile preferences are increasing, research on IT technology convergence is underway, requiring basic high-performance functions such as driver's convenience and safety. As a result, autonomous driving and semi-autonomous vehicles are developed, and these technologies sometimes deviate from lanes due to environmental problems, situations that cannot be judged by autonomous vehicles, and lane detectors may not recognize lanes. In order to improve the performance of lane departure from the lane detection system of autonomous vehicles, which is such a problem, this paper uses fast recognition, which is a characteristic of YOLO(You only look once), and is affected by the surrounding environment using CSI-Camera. We propose a lane detection system that recognizes the situation and collects driving data to extract the region of interest. [ABSTRACT FROM AUTHOR]

In this research, the accuracy of YOLO v3 algorithm in object detection during AGV (Automated Guided Vehicle) operation was investigated. First of all, AGV with 2D LiDAR and stereo camera was prepared. AGV was driven along the route scanned with SLAM (Simultaneous Localization and Mapping) using 2D LiDAR while front objects were detected through stereo camera. In order to evaluate the accuracy of YOLO v3 algorithm, recall, AP (Average Precision), and mAP (mean Average Precision) of the algorithm were measured with a degree of machine learning. Experimental results show that mAP, precision, and recall are improved by 10%, 6.8%, and 16.4%, respectively, when YOLO v3 is fitted with 4000 training dataset and 500 testing dataset which were collected through online search and is trained additionally with 1200 dataset collected from the stereo camera on AGV. [ABSTRACT FROM AUTHOR]

Recently, as the number of households raising pets increases due to the increase of single households, there is a need for a system for monitoring the status or behavior of pets. There are regional limitations in the monitoring of pets using domestic CCTVs, which requires a large number of CCTVs or restricts the behavior of pets. In this paper, we propose a mobile system for detecting and tracking cats using deep learning to solve the regional limitations of pet monitoring. We use YOLO (You Look Only Once), an object detection neural network model, to learn the characteristics of pets and apply them to Raspberry Pi to track objects detected in an image. We have designed a mobile monitoring system that connects Raspberry Pi and a laptop via wireless LAN and can check the movement and condition of cats in real time. [ABSTRACT FROM AUTHOR]