• Title/Summary/Keyword: object detection and classification

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Area Classification, Identification and Tracking for Multiple Moving Objects with the Similar Colors (유사한 색상을 지닌 다수의 이동 물체 영역 분류 및 식별과 추적)

  • Lee, Jung Sik;Joo, Yung Hoon
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.65 no.3
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    • pp.477-486
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    • 2016
  • This paper presents the area classification, identification, and tracking for multiple moving objects with the similar colors. To do this, first, we use the GMM(Gaussian Mixture Model)-based background modeling method to detect the moving objects. Second, we propose the use of the binary and morphology of image in order to eliminate the shadow and noise in case of detection of the moving object. Third, we recognize ROI(region of interest) of the moving object through labeling method. And, we propose the area classification method to remove the background from the detected moving objects and the novel method for identifying the classified moving area. Also, we propose the method for tracking the identified moving object using Kalman filter. To the end, we propose the effective tracking method when detecting the multiple objects with the similar colors. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

Forest Fire Detection System using Drone Streaming Images (드론 스트리밍 영상 이미지 분석을 통한 실시간 산불 탐지 시스템)

  • Yoosin Kim
    • Journal of Advanced Navigation Technology
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    • v.27 no.5
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    • pp.685-689
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    • 2023
  • The proposed system in the study aims to detect forest fires in real-time stream data received from the drone-camera. Recently, the number of wildfires has been increasing, and also the large scaled wildfires are frequent more and more. In order to prevent forest fire damage, many experiments using the drone camera and vision analysis are actively conducted, however there were many challenges, such as network speed, pre-processing, and model performance, to detect forest fires from real-time streaming data of the flying drone. Therefore, this study applied image data processing works to capture five good image frames for vision analysis from whole streaming data and then developed the object detection model based on YOLO_v2. As the result, the classification model performance of forest fire images reached upto 93% of accuracy, and the field test for the model verification detected the forest fire with about 70% accuracy.

Microalgae Detection Using a Deep Learning Object Detection Algorithm, YOLOv3 (딥러닝 사물 인식 알고리즘(YOLOv3)을 이용한 미세조류 인식 연구)

  • Park, Jungsu;Baek, Jiwon;You, Kwangtae;Nam, Seung Won;Kim, Jongrack
    • Journal of Korean Society on Water Environment
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    • v.37 no.4
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    • pp.275-285
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    • 2021
  • Algal bloom is an important issue in maintaining the safety of the drinking water supply system. Fast detection and classification of algae images are essential for the management of algal blooms. Conventional visual identification using a microscope is a labor-intensive and time-consuming method that often requires several hours to several days in order to obtain analysis results from field water samples. In recent decades, various deep learning algorithms have been developed and widely used in object detection studies. YOLO is a state-of-the-art deep learning algorithm. In this study the third version of the YOLO algorithm, namely, YOLOv3, was used to develop an algae image detection model. YOLOv3 is one of the most representative one-stage object detection algorithms with faster inference time, which is an important benefit of YOLO. A total of 1,114 algae images for 30 genera collected by microscope were used to develop the YOLOv3 algae image detection model. The algae images were divided into four groups with five, 10, 20, and 30 genera for training and testing the model. The mean average precision (mAP) was 81, 70, 52, and 41 for data sets with five, 10, 20, and 30 genera, respectively. The precision was higher than 0.8 for all four image groups. These results show the practical applicability of the deep learning algorithm, YOLOv3, for algae image detection.

Vehicle Classification and Tracking based on Deep Learning (딥러닝 기반의 자동차 분류 및 추적 알고리즘)

  • Hyochang Ahn;Yong-Hwan Lee
    • Journal of the Semiconductor & Display Technology
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    • v.22 no.3
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    • pp.161-165
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    • 2023
  • One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

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A Selection Method of Backbone Network through Multi-Classification Deep Neural Network Evaluation of Road Surface Damage Images (도로 노면 파손 영상의 다중 분류 심층 신경망 평가를 통한 Backbone Network 선정 기법)

  • Shim, Seungbo;Song, Young Eun
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.18 no.3
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    • pp.106-118
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    • 2019
  • In recent years, research and development on image object recognition using artificial intelligence have been actively carried out, and it is expected to be used for road maintenance. Among them, artificial intelligence models for object detection of road surface are continuously introduced. In order to develop such object recognition algorithms, a backbone network that extracts feature maps is essential. In this paper, we will discuss how to select the appropriate neural network. To accomplish it, we compared with 4 different deep neural networks using 6,000 road surface damage images. Based on three evaluation methods for analyzing characteristics of neural networks, we propose a method to determine optimal neural networks. In addition, we improved the performance through optimal tuning of hyper-parameters, and finally developed a light backbone network that can achieve 85.9% accuracy of road surface damage classification.

A SHIPBOARD MULTISENSOR SOLUTION FOR THE DETECTON OF FAST MOVING SMALL SURFACE OBJECTS

  • Ko, Hanseok
    • 제어로봇시스템학회:학술대회논문집
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    • 1995.10a
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    • pp.174-177
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    • 1995
  • Detecting a small threat object either fast moving or floating on shallow water presents a formidable challenge to shipboard sensor systems, which must determine whether or not to launch defensive weapons in a timely manner. An integrated multisensor concept is envisioned wherein the combined use of active and passive sensor is employed for the detection of short duration targets in dense ocean surface clutter to maximize detection range. The objective is to develop multisensor integration techniques that operate on detection data prior to track formation while simultaneously fusing contacts to tracks. In the system concept, detections from a low grazing angle search radar render designations to a sensor-search infrared sensor for target classification which in turn designates an active electro-optical sensor for sector search and target verification.

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Multi Modal Sensor Training Dataset for the Robust Object Detection and Tracking in Outdoor Surveillance (MMO (Multi Modal Outdoor) Dataset) (실외 경비 환경에서 강인한 객체 검출 및 추적을 위한 실외 멀티 모달 센서 기반 학습용 데이터베이스 구축)

  • Noh, DongKi;Yang, Wonkeun;Uhm, Teayoung;Lee, Jaekwang;Kim, Hyoung-Rock;Baek, SeungMin
    • Journal of Korea Multimedia Society
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    • v.23 no.8
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    • pp.1006-1018
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    • 2020
  • Dataset is getting more import to develop a learning based algorithm. Quality of the algorithm definitely depends on dataset. So we introduce new dataset over 200 thousands images which are fully labeled multi modal sensor data. Proposed dataset was designed and constructed for researchers who want to develop detection, tracking, and action classification in outdoor environment for surveillance scenarios. The dataset includes various images and multi modal sensor data under different weather and lighting condition. Therefor, we hope it will be very helpful to develop more robust algorithm for systems equipped with difference kinds of sensors in outdoor application. Case studies with the proposed dataset are also discussed in this paper.

A New Object Region Detection and Classification Method using Multiple Sensors on the Driving Environment (다중 센서를 사용한 주행 환경에서의 객체 검출 및 분류 방법)

  • Kim, Jung-Un;Kang, Hang-Bong
    • Journal of Korea Multimedia Society
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    • v.20 no.8
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    • pp.1271-1281
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    • 2017
  • It is essential to collect and analyze target information around the vehicle for autonomous driving of the vehicle. Based on the analysis, environmental information such as location and direction should be analyzed in real time to control the vehicle. In particular, obstruction or cutting of objects in the image must be handled to provide accurate information about the vehicle environment and to facilitate safe operation. In this paper, we propose a method to simultaneously generate 2D and 3D bounding box proposals using LiDAR Edge generated by filtering LiDAR sensor information. We classify the classes of each proposal by connecting them with Region-based Fully-Covolutional Networks (R-FCN), which is an object classifier based on Deep Learning, which uses two-dimensional images as inputs. Each 3D box is rearranged by using the class label and the subcategory information of each class to finally complete the 3D bounding box corresponding to the object. Because 3D bounding boxes are created in 3D space, object information such as space coordinates and object size can be obtained at once, and 2D bounding boxes associated with 3D boxes do not have problems such as occlusion.

CCTV Object Detection with Background Subtraction and Convolutional Neural Network (배경 차분과 CNN 기반의 CCTV 객체 검출)

  • Kim, Young-Min;Lee, Jiyoung;Yoon, Illo;Han, Taekjin;Kim, Chulyeon
    • KIISE Transactions on Computing Practices
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    • v.24 no.3
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    • pp.151-156
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    • 2018
  • In this paper, a method to classify objects in outdoor CCTV images using Convolutional Neural Network(CNN) and background subtraction is proposed. Object candidates are extracted using background subtraction and they are classified with CNN to detect objects in the image. At the end, computation complexity is highly reduced in comparison to other object detection algorithms. A database is constructed by filming alleys and playgrounds, places where crime occurs mainly. In experiments, different image sizes and experimental settings are tested to construct a best classifier detecting person. And the final classification accuracy became 80% for same camera data and 67.5% for a different camera.

Tillage boundary detection based on RGB imagery classification for an autonomous tractor

  • Kim, Gookhwan;Seo, Dasom;Kim, Kyoung-Chul;Hong, Youngki;Lee, Meonghun;Lee, Siyoung;Kim, Hyunjong;Ryu, Hee-Seok;Kim, Yong-Joo;Chung, Sun-Ok;Lee, Dae-Hyun
    • Korean Journal of Agricultural Science
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    • v.47 no.2
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    • pp.205-217
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    • 2020
  • In this study, a deep learning-based tillage boundary detection method for autonomous tillage by a tractor was developed, which consisted of image cropping, object classification, area segmentation, and boundary detection methods. Full HD (1920 × 1080) images were obtained using a RGB camera installed on the hood of a tractor and were cropped to 112 × 112 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the path boundary was detected using a probability map, which was generated by the integration of softmax outputs. The results show that the F1-score of the classification was approximately 0.91, and it had a similar performance as the deep learning-based classification task in the agriculture field. The path boundary was determined with edge detection and the Hough transform, and it was compared to the actual path boundary. The average lateral error was approximately 11.4 cm, and the average angle error was approximately 8.9°. The proposed technique can perform as well as other approaches; however, it only needs low cost memory to execute the process unlike other deep learning-based approaches. It is possible that an autonomous farm robot can be easily developed with this proposed technique using a simple hardware configuration.