Real-Time Vehicle and Distance Detection Based on Improved Yolo v5 Network

Wu, Tianhao; Wang, Tongwen; Liu, Yaqi

doi:10.1109/wsai51899.2021.9486316

Cited by 133 publications

(44 citation statements)

References 7 publications

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“…Yolo has been updated to version five and is regarded as the state-of-the-art algorithm for object detection ( 24 ). It has been applied in many daily life aspects, such as the detection of surface knots ( 25 ) and real-time vehicles ( 26 ), as well as in various medical fields, including face mask recognition ( 27 ), breast tumor detection and classification ( 28 ), and chest abnormality detection ( 29 ). This study showed that the basic deep learning model Yolo V5 could handle the cyst-detection task, attaining F1, precision, and mAP scores of 0.832, 0.843, and 0.821, respectively.…”

Section: Discussionmentioning

confidence: 99%

Automated detection of knee cystic lesions on magnetic resonance imaging using deep learning

Tang

Xianyue

et al. 2022

Front. Med.

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BackgroundCystic lesions are frequently observed in knee joint diseases and are usually associated with joint pain, degenerative disorders, or acute injury. Magnetic resonance imaging-based, artificial intelligence-assisted cyst detection is an effective method to improve the whole knee joint analysis. However, few studies have investigated this method. This study is the first attempt at auto-detection of knee cysts based on deep learning methods.MethodsThis retrospective study collected data from 282 subjects with knee cysts confirmed at our institution from January to October 2021. A Squeeze-and-Excitation (SE) inception attention-based You only look once version 5 (SE-YOLOv5) model was developed based on a self-attention mechanism for knee cyst-like lesion detection and differentiation from knee effusions, both characterized by high T2-weighted signals in magnetic resonance imaging (MRI) scans. Model performance was evaluated via metrics including accuracy, precision, recall, mean average precision (mAP), F1 score, and frames per second (fps).ResultsThe deep learning model could accurately identify knee MRI scans and auto-detect both obvious cyst lesions and small ones with inconspicuous contrasts. The SE-YOLO V5 model constructed in this study yielded superior performance (F1 = 0.879, precision = 0.887, recall = 0.872, all class mAP0.5 = 0.944, effusion mAP = 0.945, cyst mAP = 0.942) and improved detection speed compared to a traditional YOLO model.ConclusionThis proof-of-concept study examined whether deep learning models could detect knee cysts and distinguish them from knee effusions. The results demonstrated that the classical Yolo V5 and proposed SE-Yolo V5 models could accurately identify cysts.

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Section: Discussionmentioning

confidence: 99%

Automated detection of knee cystic lesions on magnetic resonance imaging using deep learning

Tang

Xianyue

et al. 2022

Front. Med.

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“…In 2020, Yang et al [37] showed a comparison of the results of mask detection on different models and found that the different accuracy rates are 70.40%, 77.60%, 84.60%, and 97.90% for Faster RCNN, R-FCN, SDD, and YOLOV5 respectively. In addition, Wu et al [38] offer a novel neural network structure called Yolov5-Ghost, which is based on the current Yolo v5s neural network architecture. It loses about 3% of its mAP value, and the frame rate has been increased.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

Kisaezehra¹,

Farooq²,

Bhutto³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety (OHS) is of prime importance. Like in other developing countries, this industry pays very little, rather negligible attention to OHS practices in Pakistan, resulting in the occurrence of a wide variety of accidents, mishaps, and near-misses every year. One of the major causes of such mishaps is the non-wearing of safety helmets (hard hats) at construction sites where falling objects from a height are unavoidable. In most cases, this leads to serious brain injuries in people present at the site in general and the workers in particular. It is one of the leading causes of human fatalities at construction sites. In the United States, the Occupational Safety and Health Administration (OSHA) requires construction companies through safety laws to ensure the use of well-defined personal protective equipment (PPE). It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible. However, such monitoring through technological aids or automated tools is very much possible. The present study describes a systematic strategy based on deep learning (DL) models built on the You-Only-Look-Once (YOLOV5) architecture that could be used for monitoring workers' hard hats in real-time. It can indicate whether a worker is wearing a hat or not. The proposed system uses five different models of the YOLOV5, namely YOLOV5n, YOLOv5s, YOLOv5 m, YOLOv5l, and YOLOv5x for object detection with the support of PyTorch, involving 7063 images. The results of the study show that among the DL models, the YOLOV5x has a high performance of 95.8% in terms of the mAP, while the YOLOV5n has the fastest detection speed of 70.4 frames per second (FPS). The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.

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“…YOLOv5 is a target detection algorithm open‐sourced by Ultralytics in June 2020. At present, there has been a lot of research on the original version of YOLOv5 and it has been fully described 23–26 . Therefore, this paper only introduces the improvement of the latest version of YOLOv5 R6.1.…”

Section: Preliminariesmentioning

confidence: 99%

Real‐time recognition and warning of mask wearing based on improved YOLOv5 R6.1

Yuan

Wang

Liang

et al. 2022

Int J of Intelligent Sys

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Since the new crown epidemic, mask-wearing has become a new normal in people's work and life. The inspection mechanism for mask-wearing at the entrance and exit of public places is seriously insufficient. The phenomenon of "pick-up on entry" has led to the severe formalization of mask-wearing inspection. Manual detection of mask-wearing in an open and dynamic crowded environment is unrealistic, which is not only time-consuming and labor-intensive but also cannot achieve early warning throughout the entire process. In response to this problem, this paper proposes a real-time recognition and early warning method for mask-wearing in an open, dynamic, complex environment based on improved YOLOv5 R6.1. First, replacing the first Conv structure of the backbone network in the YOLOv5 R6.1 model with an improved Stem structure to minimize the computational overhead while improving the performance. Then by normalizing the data, the random erasure data expansion technique is used to enhance the antiocclusion robustness of the algorithm. Finally, according to the mask-wearing specification in the training data set, optimizing and adjusting the anchor box parameters of the YOLOv5 R6.1 model to improve the model's ability to recognize small targets. The experiments are based on open data sets, and the results show that the mean precision (mAP), precision, and

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Real-Time Vehicle and Distance Detection Based on Improved Yolo v5 Network

Cited by 133 publications

References 7 publications

Automated detection of knee cystic lesions on magnetic resonance imaging using deep learning

Automated detection of knee cystic lesions on magnetic resonance imaging using deep learning

Real-Time Safety Helmet Detection Using Yolov5 at Construction Sites

Real‐time recognition and warning of mask wearing based on improved YOLOv5 R6.1

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