Augmented Reality Maintenance Assistant Using YOLOv5

Malta, Ana; Mendes, Mateus; Farinha, José Torres

doi:10.3390/app11114758

Cited by 96 publications

(47 citation statements)

References 13 publications

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“…YOLOv3 still presents a mature method of object detection for medical image detection (Safdar et al, 2020;Ünver & Ayan, 2019;Varçın et al, 2021). Additionally, the latest YOLO model forked into two new versions named v5s and v5m with different concepts to train, validate, and test the YAML files and additional new augmentation skills to improve performance (Malta, Mendes, and Farinha, 2021).…”

Section: Discussionmentioning

confidence: 99%

Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning

Tsai

Hung

Guo

et al. 2021

Front. Bioeng. Biotechnol.

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Background: Lumbar disc herniation (LDH) is among the most common causes of lower back pain and sciatica. The causes of LDH have not been fully elucidated but most likely involve a complex combination of mechanical and biological processes. Magnetic resonance imaging (MRI) is a tool most frequently used for LDH because it can show abnormal soft tissue areas around the spine. Deep learning models may be trained to recognize images with high speed and accuracy to diagnose LDH. Although the deep learning model requires huge numbers of image datasets to train and establish the best model, this study processed enhanced medical image features for training the small-scale deep learning dataset.Methods: We propose automatic detection to assist the initial LDH exam for lower back pain. The subjects were between 20 and 65 years old with at least 6 months of work experience. The deep learning method employed the YOLOv3 model to train and detect small object changes such as LDH on MRI. The dataset images were processed and combined with labeling and annotation from the radiologist’s diagnosis record.Results: Our method proves the possibility of using deep learning with a small-scale dataset with limited medical images. The highest mean average precision (mAP) was 92.4% at 550 images with data augmentation (550-aug), and the YOLOv3 LDH training was 100% with the best average precision at 550-aug among all datasets. This study used data augmentation to prevent under- or overfitting in an object detection model that was trained with the small-scale dataset.Conclusions: The data augmentation technique plays a crucial role in YOLOv3 training and detection results. This method displays a high possibility for rapid initial tests and auto-detection for a limited clinical dataset.

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Section: Discussionmentioning

confidence: 99%

Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning

Tsai

Hung

Guo

et al. 2021

Front. Bioeng. Biotechnol.

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“…The topic has been subject to heavy research and there have been important developments. However, most developments are just in the specific area of detection, where the result is a bounding box [ 7 ], or in the specific area of region segmentation, to tell the region of interest from the background [ 8 , 9 ]. Nonetheless, there are also a number of important developments proposing a completely integrated system, able to detect and segment tumoral masses in the pipeline with minimal human intervention.…”

Section: Mammography Imagesmentioning

confidence: 99%

Study on Data Partition for Delimitation of Masses in Mammography

2021

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Mammography is the primary medical imaging method used for routine screening and early detection of breast cancer in women. However, the process of manually inspecting, detecting, and delimiting the tumoral massess in 2D images is a very time-consuming task, subject to human errors due to fatigue. Therefore, integrated computer-aided detection systems have been proposed, based on modern computer vision and machine learning methods. In the present work, mammogram images from the publicly available Inbreast dataset are first converted to pseudo-color and then used to train and test a Mask R-CNN deep neural network. The most common approach is to start with a dataset and split the images into train and test set randomly. However, since there are often two or more images of the same case in the dataset, the way the dataset is split may have an impact on the results. Our experiments show that random partition of the data can produce unreliable training, so the dataset must be split using case-wise partition for more stable results. In experimental results, the method achieves an average true positive rate of 0.936 with 0.063 standard deviation using random partition and 0.908 with 0.002 standard deviation using case-wise partition, showing that case-wise partition must be used for more reliable results.

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“…YOLO may response in real-time even running on computationally limited devices because it determines a prediction by executing only one forward propagation through the neural network. The algorithm has been upgraded into its versions of YOLOv3 [13], YOLOv4 [14], and YOLOv5 [15]. For sure, it would be beneficial to the road maintenance community if the best fit model for pothole detection is provided into the road maintenance arsenals.…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution of this paper is to find out the best fit model that allows efficiently heterogenous damage (i.e., pothole) detection on the surface of road. The mean average precision at 50% Intersection-over-Union threshold (mAP_0.5) for each YOLOv4 [14], YOLOv4-tiny [16], and YOLOv5s [15] models is set as a measure of performance. The research was conducted in six steps.…”

Section: Introductionmentioning

confidence: 99%

Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection

2021

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Pothole repair is one of the paramount tasks in road maintenance. Effective road surface monitoring is an ongoing challenge to the management agency. The current pothole detection, which is conducted image processing with a manual operation, is labour-intensive and time-consuming. Computer vision offers a mean to automate its visual inspection process using digital imaging, hence, identifying potholes from a series of images. The goal of this study is to apply different YOLO models for pothole detection. Three state-of-the-art object detection frameworks (i.e., YOLOv4, YOLOv4-tiny, and YOLOv5s) are experimented to measure their performance involved in real-time responsiveness and detection accuracy using the image set. The image set is identified by running the deep convolutional neural network (CNN) on several deep learning pothole detectors. After collecting a set of 665 images in 720 × 720 pixels resolution that captures various types of potholes on different road surface conditions, the set is divided into training, testing, and validation subsets. A mean average precision at 50% Intersection-over-Union threshold (mAP_0.5) is used to measure the performance of models. The study result shows that the mAP_0.5 of YOLOv4, YOLOv4-tiny, and YOLOv5s are 77.7%, 78.7%, and 74.8%, respectively. It confirms that the YOLOv4-tiny is the best fit model for pothole detection.

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Augmented Reality Maintenance Assistant Using YOLOv5

Cited by 96 publications

References 13 publications

Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning

Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning

Study on Data Partition for Delimitation of Masses in Mammography

Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection

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