Research on Detecting Bearing-Cover Defects Based on Improved YOLOv3

Zheng, Zehao; Zhao, Ji; Li, Yue

doi:10.1109/access.2021.3050484

Cited by 45 publications

(16 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shaikh et al [13] presented a common technique background subtraction for accurately detecting moving objects in videos taken by stationary cameras. Zheng et al [14] offer an enhanced YOLOv3 network model and build a large-scale bearing-cover defect dataset. Wang et al [15] addressed the issue of image background and foreground imbalance by incorporating the foreground and background balance loss function into the YOLOv4 loss function component.…”

Section: Research Contributionsmentioning

confidence: 99%

Object Classification and Tracking Using Scaled P8 YOLOv4 Lite Model

Shaikh

Chopade

Kharate

2023

Period. Polytech. Elec. Eng. Comp. Sci.

View full text Add to dashboard Cite

One of the most difficult tasks in the area of computer vision is object detection, which combines object categorization and object location within a scene. In terms of object detection, Deep Neural Networks have been recently demonstrated to outperform alternative approaches. The issues related deep learning neural network is its complexity and huge computation, so it is not possible to detect and track the objects in image of high resolution in real time. We proposed scaled YOLOv4 lite model as Single Stage Detector Neural Network for object detection, tracking and it is trained using COCO 2017 dataset. To create the YOLOv4-CSP- P5- P6- P7- P8 networks, the Scaled YOLOv4 applied efficient network scaling strategies. The additional layer in YOLOv4 lite model is added as P8 layer which improves accuracy. Cross-stage-partial (CSP) connections and Mish activation are used in improved network design, such as backbone optimization and Neck (PAN). In the case of YOLOv4, however, it can only be trained once for all resolutions. Width and Height activations have been changed, allowing for faster network training. With YOLOv4 lite model, we used CSPDarkNet-53 model as a backbone. The experimental result show our YOLOv4 lite model can detect and track object up to 28 fps when model run with the video input and has an accuracy of 86.09% when tested on real-time video with resolutions 1920 × 1080 (full HD). AP = 50.81%, AP @50 = 63.6%, and AP @75 = 52.5% for CSPDarkNet-53 model backbone.

show abstract

Section: Research Contributionsmentioning

confidence: 99%

Object Classification and Tracking Using Scaled P8 YOLOv4 Lite Model

Shaikh

Chopade

Kharate

2023

Period. Polytech. Elec. Eng. Comp. Sci.

View full text Add to dashboard Cite

show abstract

“…The frame rate for a 448 x 448-pixel image is 45 fps (0.022 seconds per image) on the Titan X GPU while achieving advanced mAP (precision average). Yolov3 has several stages in classifying detection YOLOv3 feature extraction uses the darknet to predict the class and location of objects, after which YOLOv3 will classify objects according to their class [16] [17].…”

Section: You Only Look Once V3mentioning

confidence: 99%

Design of A Real-Time Object Detection Prototype System with YOLOv3 (You Only Look Once)

Gunawan

Nurdin

Fajriana

2022

Int. J. Eng. Scie. and Inform. Technology.

View full text Add to dashboard Cite

Object detection is an activity that aims to gain an understanding of the classification, concept estimation, and location of objects in an image. As one of the fundamental computer vision problems, object detection can provide valuable information for the semantic understanding of images and videos and is associated with many applications, including image classification. Object detection has recently become one of the most exciting fields in computer vision. Detection of objects on this system using YOLOv3. The You Only Look Once (YOLO) method is one of the fastest and most accurate methods for object detection and is even capable of exceeding two times the capabilities of other algorithms. You Only Look Once, an object detection method, is very fast because a single neural network predicts bounded box and class probabilities directly from the whole image in an evaluation. In this study, the object under study is an object that is around the researcher (a random thing). System design using Unified Modeling Language (UML) diagrams, including use case diagrams, activity diagrams, and class diagrams. This system will be built using the python language. Python is a high-level programming language that can execute some multi-use instructions directly (interpretively) with the Object Oriented Programming method and also uses dynamic semantics to provide a level of syntax readability. As a high-level programming language, python can be learned easily because it has been equipped with automatic memory management, where the user must run through the Anaconda prompt and then continue using Jupyter Notebook. The purpose of this study was to determine the accuracy and performance of detecting random objects on YOLOv3. The result of object detection will display the name and bounding box with the percentage of accuracy. In this study, the system is also able to recognize objects when they object is stationary or moving.

show abstract

“…Zehao Zheng [9] proposed a YOLOv3 model to detect Bearing-Cover with mAP based 69.74% accuracy. In this approach, a total of 9861 image data is used, and also labeling is done by all of the images.…”

Section: Payal Bose [6] Et Al Have Implemented Medicinal Plants Leaf ...mentioning

confidence: 99%

Comprehensive Analysis of CNN and YOLOv5 Object Detection Model to Classify Phytomedicine Tree’s Leaf Disease

Islam

Haque

Rahaman

et al. 2022

Preprint

View full text Add to dashboard Cite

Phytomedicine is not only known to the poor people of East Bengal and West Bengal as a medicine but also as a beacon of hope for survival in a diseased life. In this approach, Terminalia Arjuna, Lawsonia inermis, Azadirachta indica, and Ocimum tenuiflorum Phytomedicine tree datasets. Terminalia Arjuna mainly protects gastric mucosa from oxidative damage and it makes it strong so that the stomach gets protection against ulcers. It also exhibits strong antibacterial and antimicrobial properties which help to fight against stomach infections like gastric, diarrhea, and dysentery. There are various uses of Lawsonia inermis. It is known as a powerful detoxifier and it helps to prefer healthy gum, and healthy hair and reduces arthritis pain. The flower of Lawsonia inermis can be used for headaches due to the heat of the sun. Neem is known as Phytomedicine for all diseases and the most common diseases are treated with acne, nourishing the skin, treating wounds, use to reduce dandruff and joint pain, etc. Mainly it increases immunity and provides a healthy life. Another famous Phytomedicine known as Ocimum tenuiflorum is used to treat kidney stones, alleviate fever and headaches, provide a healthy heart, healthy eye, healthy oral, and fight against acne, etc. It is also known as a big source of vitamin K. The most famous and fast object detection algorithms as YOLOv5 and Keras pre-trained models as Xception, ResNet152V2, VGG19, and InceptionResNetV2 have been developed to predict disease-affected or disease-free leaves. The implemented approach has gained a height accuracy of 97.00% from Xception and 95% accuracy from YOLOv5.

show abstract

Research on Detecting Bearing-Cover Defects Based on Improved YOLOv3

Cited by 45 publications

References 32 publications

Object Classification and Tracking Using Scaled P8 YOLOv4 Lite Model

Object Classification and Tracking Using Scaled P8 YOLOv4 Lite Model

Design of A Real-Time Object Detection Prototype System with YOLOv3 (You Only Look Once)

Comprehensive Analysis of CNN and YOLOv5 Object Detection Model to Classify Phytomedicine Tree’s Leaf Disease

Contact Info

Product

Resources

About