• Journal of Information Processing Systems
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• v.18 no.4
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• pp.524-534
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• 2022

In this paper, we produce Trash Object Detection (TOD) dataset to solve trash detection problems. A well-organized dataset of sufficient size is essential to train object detection models and apply them to specific tasks. However, existing trash datasets have only a few hundred images, which are not sufficient to train deep neural networks. Most datasets are classification datasets that simply classify categories without location information. In addition, existing datasets differ from the actual guidelines for separating and discharging recyclables because the category definition is primarily the shape of the object. To address these issues, we build and experiment with trash datasets larger than conventional trash datasets and have more than twice the resolution. It was intended for general household goods. And annotated based on guidelines for separating and discharging recyclables from the Ministry of Environment. Our dataset has 10 categories, and around 33K objects were annotated for around 5K images with 1280×720 resolution. The dataset, as well as the pre-trained models, have been released at https://github.com/jms0923/tod.

• International journal of advanced smart convergence
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• v.13 no.2
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• pp.195-204
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• 2024

In this paper, we investigate the comparative performance of two leading object detection architectures, YOLOv7 and Detection Transformer (DETR), across varying levels of data augmentation using CycleGAN. Our experiments focus on chest scan images within the context of biomedical informatics, specifically targeting the detection of abnormalities. The study reveals that YOLOv7 consistently outperforms DETR across all levels of augmented data, maintaining better performance even with 75% augmented data. Additionally, YOLOv7 demonstrates significantly faster convergence, requiring approximately 30 epochs compared to DETR's 300 epochs. These findings underscore the superiority of YOLOv7 for object detection tasks, especially in scenarios with limited data and when rapid convergence is essential. Our results provide valuable insights for researchers and practitioners in the field of computer vision, highlighting the effectiveness of YOLOv7 and the importance of data augmentation in improving model performance and efficiency.

• Journal of the Korea Society of Computer and Information
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• v.27 no.1
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• pp.1-8
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• 2022

In this paper, we propose a fire detection system based on CCTV images using an object tracking technology with YOLOv4 model capable of real-time object detection and a DeepSORT algorithm. The fire detection model was learned from 10800 pieces of learning data and verified through 1,000 separate test sets. Subsequently, the fire detection rate in a single image and fire detection maintenance performance in the image were increased by tracking the detected fire area through the DeepSORT algorithm. It is verified that a fire detection rate for one frame in video data or single image could be detected in real time within 0.1 second. In this paper, our AI fire detection system is more stable and faster than the existing fire accident detection system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.410-412
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• 2021

This paper proposes an improved pedestrian detection method using object and background histograms. Objects detected through the HOG & SVM algorithm are detected in a square shape. Inside the square area, the background and the object area are mixed. If only the area of the object excluding the background is detected, various object-related information may be easily obtained. The size of the detected rectangle is readjusted using an xy-axis projection algorithm to fit the size of the object. And then, the improved object is detected by dividing the background and the object based on the histogram of the object in the readjusted square. The average values of precision and recall, which are reliability evaluations comparing the detected object with the original object, are 97.9% and 90%, respectively.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.183-189
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• 2015

In this paper, we propose an object tracking method using the color information of the image in surveillance network. This method perform a object detection using of adaptive color model. Object contour detection plays an important role in application such as object recognition. Experimental results demonstrate successful object detection over a wide range of object's variation in color and scale. In applications to detect an object in real time, when transmitting a large amount of image data it is possible to find the mode of a color distribution. The specific color of an object is modified at dynamically changing color in image. So, this algorithm detects the tracking area information of object within relevant tracking area and only tracking the movement of that object.Through experiments, we show that proposed method is more robust than other methods under certain ideal situations.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.232-238
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• 2020

In this paper, we study on aerial objects detection and position estimation algorithm for the safety of UAV that flight in BVLOS. We use the vision sensor and LiDAR to detect objects. We use YOLOv2 architecture based on CNN to detect objects on a 2D image. Additionally we use a clustering method to detect objects on point cloud data acquired from LiDAR. When a single sensor used, detection rate can be degraded in a specific situation depending on the characteristics of sensor. If the result of the detection algorithm using a single sensor is absent or false, we need to complement the detection accuracy. In order to complement the accuracy of detection algorithm based on a single sensor, we use the Kalman filter. And we fused the results of a single sensor to improve detection accuracy. We estimate the 3D position of the object using the pixel position of the object and distance measured to LiDAR. We verified the performance of proposed fusion algorithm by performing the simulation using the Gazebo simulator.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.589-596
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• 2013

With the increase of social crime rate, the interest on the intelligent security system is also growing. This paper proposes a detection system of monitoring whether abnormal behavior is being carried in the images captured using CCTV. After detection of an object via subtraction from background image and morpholgy, this system extracts an abnormal behavior by each object's feature information and its trajectory. When an object is loitering for a while in CCTV images, this system considers the loitering as an abnormal behavior and sends the alarm signal to the control center to facilitate prevention in advance. Especially, this research aims at detecting a loitoring act among various abnormal behaviors and also extends to the detection whether an incoming object is identical to one of inactive objects out of image.

• Journal of the Semiconductor & Display Technology
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• v.16 no.3
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• pp.78-81
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• 2017

Augmented Reality(AR) is the core technology of the future knowledge service industry. It is expected to be used in various fields such as medical, education, entertainment etc. Briefly, augmented reality technology is a technique in which a mapped virtual object is augmented when a real-world object is viewed through a device after mapping a real-world object and a virtual object. In this paper, we implemented object detection and tracking system, which is a key technology of augmented reality. To speed up the object tracking, the ORB algorithm, which is a lightweight algorithm compared to the detection algorithm, is applied. In addition, KNN classifier, which is a machine learning algorithm, was applied to detect a single object by learning multiple images.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.125-132
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• 2023

Roundabouts have strengths in traffic flow and safety but can present difficulties for inexperienced drivers. Demand to acquire and analyze drone images has increased to enhance a traffic environment allowing drivers to deal with roundabouts easily. In this paper, we propose a roundabout traffic analysis system that detects, tracks, and analyzes vehicles using a deep learning-based object detection model (YOLOv7) in drone images. About 3600 images for object detection model learning and testing were extracted and labeled from 1 hour of drone video. Through training diverse conditions and evaluating the performance of object detection models, we achieved an average precision (AP) of up to 97.2%. In addition, we utilized SORT (Simple Online and Realtime Tracking) and OC-SORT (Observation-Centric SORT), a real-time object tracking algorithm, which resulted in an average MOTA (Multiple Object Tracking Accuracy) of up to 89.2%. By implementing a method for measuring roundabout entry speed, we achieved an accuracy of 94.5%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.693-698
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• 2018

This paper proposes a vehicle detection method for parking areas using unmanned aerial vehicles (UAVs) and using YOLOv2, which is a recent, known, fast, object-detection real-time algorithm. The YOLOv2 convolutional network algorithm can calculate the probability of each class in an entire image with a one-pass evaluation, and can also predict the location of bounding boxes. It has the advantage of very fast, easy, and optimized-at-detection performance, because the object detection process has a single network. The sliding windows methods and region-based convolutional neural network series detection algorithms use a lot of region proposals and take too much calculation time for each class. So these algorithms have a disadvantage in real-time applications. This research uses the YOLOv2 algorithm to overcome the disadvantage that previous algorithms have in real-time processing problems. Using Darknet, OpenCV, and the Compute Unified Device Architecture as open sources for object detection. a deep learning server is used for the learning and detecting process with each car. In the experiment results, the algorithm could detect cars in a dense area using UAVs, and reduced overhead for object detection. It could be applied in real time.