• Title/Summary/Keyword: YOLOv5 model

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A Performance Comparison of Land-Based Floating Debris Detection Based on Deep Learning and Its Field Applications (딥러닝 기반 육상기인 부유쓰레기 탐지 모델 성능 비교 및 현장 적용성 평가)

  • Suho Bak;Seon Woong Jang;Heung-Min Kim;Tak-Young Kim;Geon Hui Ye
    • Korean Journal of Remote Sensing
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    • v.39 no.2
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    • pp.193-205
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    • 2023
  • A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.

A Fire Deteetion System based on YOLOv5 using Web Camera (웹카메라를 이용한 YOLOv5 기반 화재 감지 시스템)

  • Park, Dae-heum;Jang, Si-woong
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.10a
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    • pp.69-71
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    • 2022
  • Today, the AI market is very large due to the development of AI. Among them, the most advanced AI is image detection. Thus, there are many object detection models using YOLOv5.However, most object detection in AI is focused on detecting objects that are stereotyped.In order to recognize such unstructured data, the object may be recognized by learning and filtering the object. Therefore, in this paper, a fire monitoring system using YOLOv5 was designed to detect and analyze unstructured data fires and suggest ways to improve the fire object detection model.

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A Research of a Traffic Light Signal Classification Model using YOLOv5 for Autonomous Driving (자율주행을 위한 YOLOv5 기반 신호등의 신호 분류 모델 연구)

  • Joongjin Kook;Hakseung Lee
    • Journal of the Semiconductor & Display Technology
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    • v.23 no.1
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    • pp.61-64
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    • 2024
  • As research on autonomous driving technology becomes more active, various studies on signal recognition of traffic lights are also being conducted. When recognizing traffic lights with different purposes and shapes, such as pedestrian traffic lights, vehicle-only traffic lights, and right-turn traffic lights, existing classification methods may cause misrecognition problems. Therefore, in this study, we studied a model that allows accurate signal recognition by subdividing the classification of signals according to the purpose and type of traffic lights. A signal recognition model was created by classifying traffic lights according to their shape and purpose into horizontal, vertical, right turn, etc., and by comparing them with the existing signal recognition model based on YOLOv5, it was confirmed that more correct and accurate recognition was possible.

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Sorghum Panicle Detection using YOLOv5 based on RGB Image Acquired by UAV System (무인기로 취득한 RGB 영상과 YOLOv5를 이용한 수수 이삭 탐지)

  • Min-Jun, Park;Chan-Seok, Ryu;Ye-Seong, Kang;Hye-Young, Song;Hyun-Chan, Baek;Ki-Su, Park;Eun-Ri, Kim;Jin-Ki, Park;Si-Hyeong, Jang
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.24 no.4
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    • pp.295-304
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    • 2022
  • The purpose of this study is to detect the sorghum panicle using YOLOv5 based on RGB images acquired by a unmanned aerial vehicle (UAV) system. The high-resolution images acquired using the RGB camera mounted in the UAV on September 2, 2022 were split into 512×512 size for YOLOv5 analysis. Sorghum panicles were labeled as bounding boxes in the split image. 2,000images of 512×512 size were divided at a ratio of 6:2:2 and used to train, validate, and test the YOLOv5 model, respectively. When learning with YOLOv5s, which has the fewest parameters among YOLOv5 models, sorghum panicles were detected with mAP@50=0.845. In YOLOv5m with more parameters, sorghum panicles could be detected with mAP@50=0.844. Although the performance of the two models is similar, YOLOv5s ( 4 hours 35 minutes) has a faster training time than YOLOv5m (5 hours 15 minutes). Therefore, in terms of time cost, developing the YOLOv5s model was considered more efficient for detecting sorghum panicles. As an important step in predicting sorghum yield, a technique for detecting sorghum panicles using high-resolution RGB images and the YOLOv5 model was presented.

Metal Surface Defect Detection and Classification using EfficientNetV2 and YOLOv5 (EfficientNetV2 및 YOLOv5를 사용한 금속 표면 결함 검출 및 분류)

  • Alibek, Esanov;Kim, Kang-Chul
    • The Journal of the Korea institute of electronic communication sciences
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    • v.17 no.4
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    • pp.577-586
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    • 2022
  • Detection and classification of steel surface defects are critical for product quality control in the steel industry. However, due to its low accuracy and slow speed, the traditional approach cannot be effectively used in a production line. The current, widely used algorithm (based on deep learning) has an accuracy problem, and there are still rooms for development. This paper proposes a method of steel surface defect detection combining EfficientNetV2 for image classification and YOLOv5 as an object detector. Shorter training time and high accuracy are advantages of this model. Firstly, the image input into EfficientNetV2 model classifies defect classes and predicts probability of having defects. If the probability of having a defect is less than 0.25, the algorithm directly recognizes that the sample has no defects. Otherwise, the samples are further input into YOLOv5 to accomplish the defect detection process on the metal surface. Experiments show that proposed model has good performance on the NEU dataset with an accuracy of 98.3%. Simultaneously, the average training speed is shorter than other models.

Real-time traffic light information recognition based on object detection models (객체 인식 모델 기반 실시간 교통신호 정보 인식)

  • Joo, eun-oh;Kim, Min-Soo
    • Journal of Cadastre & Land InformatiX
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    • v.52 no.1
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    • pp.81-93
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    • 2022
  • Recently, there have been many studies on object recognition around the vehicle and recognition of traffic signs and traffic lights in autonomous driving. In particular, such the recognition of traffic lights is one of the core technologies in autonomous driving. Therefore, many studies for such the recognition of traffic lights have been performed, the studies based on various deep learning models have increased significantly in recent. In addition, as a high-quality AI training data set for voice, vision, and autonomous driving is released on AIHub, it makes it possible to develop a recognition model for traffic lights suitable for the domestic environment using the data set. In this study, we developed a recognition model for traffic lights that can be used in Korea using the AIHub's training data set. In particular, in order to improve the recognition performance, we used various models of YOLOv4 and YOLOv5, and performed our recognition experiments by defining various classes for the training data. In conclusion, we could see that YOLOv5 shows better performance in the recognition than YOLOv4 and could confirm the reason from the architecture comparison of the two models.

A Study on Falling Detection of Workers in the Underground Utility Tunnel using Dual Deep Learning Techniques (이중 딥러닝 기법을 활용한 지하공동구 작업자의 쓰러짐 검출 연구)

  • Jeongsoo Kim;Sangmi Park;Changhee Hong
    • Journal of the Society of Disaster Information
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    • v.19 no.3
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    • pp.498-509
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    • 2023
  • Purpose: This paper proposes a method detecting the falling of a maintenance worker in the underground utility tunnel, by applying deep learning techniques using CCTV video, and evaluates the applicability of the proposed method to the worker monitoring of the utility tunnel. Method: Each rule was designed to detect the falling of a maintenance worker by using the inference results from pre-trained YOLOv5 and OpenPose models, respectively. The rules were then integrally applied to detect worker falls within the utility tunnel. Result: Although the worker presence and falling were detected by the proposed model, the inference results were dependent on both the distance between the worker and CCTV and the falling direction of the worker. Additionally, the falling detection system using YOLOv5 shows superior performance, due to its lower dependence on distance and fall direction, compared to the OpenPose-based. Consequently, results from the fall detection using the integrated dual deep learning model were dependent on the YOLOv5 detection performance. Conclusion: The proposed hybrid model shows detecting an abnormal worker in the utility tunnel but the improvement of the model was meaningless compared to the single model based YOLOv5 due to severe differences in detection performance between each deep learning model

Influence of Self-driving Data Set Partition on Detection Performance Using YOLOv4 Network (YOLOv4 네트워크를 이용한 자동운전 데이터 분할이 검출성능에 미치는 영향)

  • Wang, Xufei;Chen, Le;Li, Qiutan;Son, Jinku;Ding, Xilong;Song, Jeongyoung
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.20 no.6
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    • pp.157-165
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    • 2020
  • Aiming at the development of neural network and self-driving data set, it is also an idea to improve the performance of network model to detect moving objects by dividing the data set. In Darknet network framework, the YOLOv4 (You Only Look Once v4) network model was used to train and test Udacity data set. According to 7 proportions of the Udacity data set, it was divided into three subsets including training set, validation set and test set. K-means++ algorithm was used to conduct dimensional clustering of object boxes in 7 groups. By adjusting the super parameters of YOLOv4 network for training, Optimal model parameters for 7 groups were obtained respectively. These model parameters were used to detect and compare 7 test sets respectively. The experimental results showed that YOLOv4 can effectively detect the large, medium and small moving objects represented by Truck, Car and Pedestrian in the Udacity data set. When the ratio of training set, validation set and test set is 7:1.5:1.5, the optimal model parameters of the YOLOv4 have highest detection performance. The values show mAP50 reaching 80.89%, mAP75 reaching 47.08%, and the detection speed reaching 10.56 FPS.

Implementation of YOLOv5-based Forest Fire Smoke Monitoring Model with Increased Recognition of Unstructured Objects by Increasing Self-learning data

  • Gun-wo, Do;Minyoung, Kim;Si-woong, Jang
    • International Journal of Advanced Culture Technology
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    • v.10 no.4
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    • pp.536-546
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    • 2022
  • A society will lose a lot of something in this field when the forest fire broke out. If a forest fire can be detected in advance, damage caused by the spread of forest fires can be prevented early. So, we studied how to detect forest fires using CCTV currently installed. In this paper, we present a deep learning-based model through efficient image data construction for monitoring forest fire smoke, which is unstructured data, based on the deep learning model YOLOv5. Through this study, we conducted a study to accurately detect forest fire smoke, one of the amorphous objects of various forms, in YOLOv5. In this paper, we introduce a method of self-learning by producing insufficient data on its own to increase accuracy for unstructured object recognition. The method presented in this paper constructs a dataset with a fixed labelling position for images containing objects that can be extracted from the original image, through the original image and a model that learned from it. In addition, by training the deep learning model, the performance(mAP) was improved, and the errors occurred by detecting objects other than the learning object were reduced, compared to the model in which only the original image was learned.

A Study on the A.I Detection Model of Marine Deposition Waste Using YOLOv5 (YOLOv5를 이용한 해양 침적쓰레기 검출 A.I 모델에 대한 연구)

  • Wang, Tae-su;Oh, Seyeong;Lee, Hyeon-seo;Jang, Jongwook;Kim, Minyoung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2021.10a
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    • pp.385-387
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    • 2021
  • Marine deposition waste threatens the book ecosystem and causes a decrease in catch due to ghost fishing, causing damage of about 370 billion won per year. In order to collect this, a current status survey is conducted using two-way ultrasonic detectors, diving, and lifting frames. However, the scope of the investigation is small to investigate a lot of sedimentary waste, and there is a possibility of causing casualties. This paper deals with the implementation of a high-accuracy marine deposition detection AI model by learning the coastal sediment image data of AI-Hub using the YOLOv5 algorithm suitable for real-time object detection.

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