Object Detection using Convolutional Neural Networks for Smart Manufacturing Vision Systems in the Medical Devices Sector

O’Brien, Kelly; Humphries, Jacqueline

doi:10.1016/j.promfg.2020.01.019

Cited by 8 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another research direction is to use computer vision for social distancing and masking while manufacturing to reduce the risk of transmission [25]. object detection in medical manufacturing using computer vision has been examined for quality checks [26]. Moreover, several works have been checking for any defects in the used materials [27,28].…”

Section: B Existing Studiesmentioning

confidence: 99%

Smart Industrial Safety using Computer Vision

Bhana,

Mahmoud,

Idrissi

2023

2023 28th International Conference on Automation and Computing (ICAC)

View full text Add to dashboard Cite

More than 2.3 million people worldwide suffer from work-related injuries or illnesses each year, resulting in more than 6,000 deaths per day. Providing an unclear work environment and failing to wear appropriate personal protective equipment have been identified as significant contributors to workplace accidents, making it imperative that employers prioritize workplace safety as a priority. Providing proper personal protective equipment (PPE) and maintaining a well-organized, clearly marked (unsafe) work environment can help prevent inconvenient workplace incidents. Furthermore, it promotes a safe working environment, reduces the likelihood of life-threatening events, and enhances overall business and economic conditions. Therefore, this paper proposes safe, smart manufacturing by implementing computer vision technology to detect appropriate PPE worn by workers and ensure a safe workspace to reduce the risk of human injuries. By utilising computer vision technology, we can identify PPE, such as gloves, helmets, and working forklifts, used by workers in the manufacturing environment. A precision of 80.6% and 86% have been reached using YOLOv8 for all classes in both datasets. In general, an extensive review of both datasets, including five performance metrics, is considered.

show abstract

Section: B Existing Studiesmentioning

confidence: 99%

Smart Industrial Safety using Computer Vision

Bhana,

Mahmoud,

Idrissi

2023

2023 28th International Conference on Automation and Computing (ICAC)

View full text Add to dashboard Cite

show abstract

“…Vision systems are seldom employed in the quality control of medical device manufacturing due to their relatively high error rate caused by sensitivity to light and setup [94]. Hence, these kinds of setups often require expert validation and are considered unreliable.…”

Section: Data-driven Decision Makingmentioning

confidence: 99%

“…Hence, these kinds of setups often require expert validation and are considered unreliable. However, a study conducted by [94] studied medical device classification in different settings using CNNs and identified Single Shot Multibox (SSD) model, as preferred classifier in medical device production. Furthermore, real time quality inspection can take place by collecting acoustic, visual, and haptic signals from wearable smart devices for CNN model which qualifies the task action as successful or not [95].…”

Section: Data-driven Decision Makingmentioning

confidence: 99%

A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing

Lepasepp

Hurst

2021

Future Internet

View full text Add to dashboard Cite

Ever since the emergence of Industry 4.0 as the synonymous term for the fourth industrial revolution, its applications have been widely discussed and used in many business scenarios. This concept is derived from the advantages of internet and technology, and it describes the efficient synchronicity of humans and computers in smart factories. By leveraging big data analysis, machine learning and robotics, the end-to-end supply chain is optimized in many ways. However, these implementations are more challenging in heavily regulated fields, such as medical device manufacturing, as incorporating new technologies into factories is restricted by the regulations in place. Moreover, the production of medical devices requires an elaborate quality analysis process to assure the best possible outcome to the patient. Therefore, this article reflects on the benefits (features) and limitations (obstacles), in addition to the various smart manufacturing trends that could be implemented within the medical device manufacturing field by conducting a systematic literature review of 104 articles sourced from four digital libraries. Out of the 7 main themes and 270 unique applied technologies, 317 features and 117 unique obstacles were identified. Furthermore, the main findings include an overview of ways in which manufacturing could be improved and optimized within a regulated setting, such as medical device manufacturing.

show abstract

Using Neural Network to Optimize Bin-Picking in the SME Manufacturing Digital Transformation

Juhe,

Dossou

2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Object Detection using Convolutional Neural Networks for Smart Manufacturing Vision Systems in the Medical Devices Sector

Cited by 8 publications

References 5 publications

Smart Industrial Safety using Computer Vision

Smart Industrial Safety using Computer Vision

A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing

Using Neural Network to Optimize Bin-Picking in the SME Manufacturing Digital Transformation

Contact Info

Product

Resources

About