DERIN ÖĞRENME TABANLI İŞYERI KAMERASI ILE GERÇEK ZAMANLI KIŞISEL KORUYUCU EKIPMAN VE DEPO GÜVENLIĞI TESPITI

Abdil Karakan; Yüksel Oğuz

doi:10.54365/adyumbd.1470598

TR EN

DERIN ÖĞRENME TABANLI İŞYERI KAMERASI ILE GERÇEK ZAMANLI KIŞISEL KORUYUCU EKIPMAN VE DEPO GÜVENLIĞI TESPITI

Öz

İş kazalarının büyük bir çoğunluğu basit tedbirlerle önlenebilecek seviyededir. Bunların başında çalışanların kullanması gereken kişisel koruyucu ekipmanları gelmektedir. Yapılan çalışmada bir iş yeri kamerasından alınan görüntüler ile gerçek zamanlı olarak kişisel koruyucu ekipmanlarının tespiti gerçekleştirilmiştir. Bunun için iş yeri kamerasından alınan görüntülerden bir veri seti oluşturulmuştur. Bu veri seti 6125 tane fotoğraftan oluşmaktadır. Ayrıca fotoğraflar üzerinde gri tonlama, eğim eklenmesi, bulanıklaştırma, değişkenlik eklenmesi, gürültü eklenmesi, görüntü parlaklığı değişikliği, renk canlılığı değişikliği, perspektif değişikliği, boyutlandırma ve konum değişikliği eklenmiştir. Bu eklemeler ile kameradan meydana gelebilecek herhangi bir bozulmaya karşı oluşacak hata en aza indirilmiştir. Fotoğraflar üzerinde yapılan değişiklikler ile veri setini oluşturan fotoğraf sayısı 21079’a yükselmiştir. Oluşturulan veri seti YOLOv5 mimarisinde çalıştırılmıştır. Çalışmada kask, ayakkabı, yelek, yolda, yolda değil, yeleksiz, ayakkabısız, apron ve kasksız olmak üzere 9 çeşit kişisel koruyucu ekipmanın tespiti gerçekleştirilmiştir. Çalışma sonucunda ortalama doğruluk 97.3%, mAP 93.8% ve recall 91.7% gerçekleşmiştir.

Anahtar Kelimeler

Derin öğrenme , Kişisel koruyucu ekipman , Gerçek zamanlı nesne algılama , YOLO mimarisi , Depo güvenliği.

REAL-TIME PERSONAL PROTECTIVE EQUIPMENT AND WAREHOUSE SAFETY DETECTION WITH DEEP LEARNING-BASED WORKPLACE CAMERA

Abstract

The majority of work accidents can be prevented with simple precautions. The most important of these is the personal protective equipment that employees must use. In the study, personal protective equipment and warehouse security were detected in real time with images taken from a workplace camera. For this purpose, a data set was created from images taken from the workplace camera. This data set consists of 6125 photographs. Additionally, grayscale, tilt addition, blurring, variability addition, noise addition, image brightness change, color vibrancy change, perspective change, resizing and position change have been added to the photographs. With these additions, the error that may occur due to any distortion that may occur from the camera is minimized. With the changes made to the photographs, the number of photographs forming the data set increased to 21079. The created data set was run on YOLOv8 architecture. In the study, 9 types of personal protective equipment and warehouse safety were determined: helmet, shoes, vest, on the road, not on the road, without vest, without shoes, apron and without helmet. As a result of the study, average stability was 97.3%, mAP was 93.8% and recall was 91.7%.

Keywords

Deep learning , Personal protective equipment , Real-time object detection , YOLO architecture , Warehouse security.

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Details

Primary Language

Turkish

Subjects

Electrical Engineering (Other)

Journal Section

Research Article

Authors

Abdil Karakan ^*
0000-0003-1651-7568
Türkiye

Yüksel Oğuz
0000-0002-5233-151X
Türkiye

Early Pub Date

December 29, 2024

Publication Date

December 31, 2024

Submission Date

April 27, 2024

Acceptance Date

September 30, 2024

Published in Issue

Year 2024 Volume: 11 Number: 24

DOI

https://doi.org/10.54365/adyumbd.1470598

IZ

https://izlik.org/JA59LJ49LK

APA

Karakan, A., & Oğuz, Y. (2024). DERIN ÖĞRENME TABANLI İŞYERI KAMERASI ILE GERÇEK ZAMANLI KIŞISEL KORUYUCU EKIPMAN VE DEPO GÜVENLIĞI TESPITI. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 11(24), 402-414. https://doi.org/10.54365/adyumbd.1470598

AMA

1.Karakan A, Oğuz Y. DERIN ÖĞRENME TABANLI İŞYERI KAMERASI ILE GERÇEK ZAMANLI KIŞISEL KORUYUCU EKIPMAN VE DEPO GÜVENLIĞI TESPITI. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2024;11(24):402-414. doi:10.54365/adyumbd.1470598

Chicago

Karakan, Abdil, and Yüksel Oğuz. 2024. “DERIN ÖĞRENME TABANLI İŞYERI KAMERASI ILE GERÇEK ZAMANLI KIŞISEL KORUYUCU EKIPMAN VE DEPO GÜVENLIĞI TESPITI”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 11 (24): 402-14. https://doi.org/10.54365/adyumbd.1470598.

EndNote

Karakan A, Oğuz Y (December 1, 2024) DERIN ÖĞRENME TABANLI İŞYERI KAMERASI ILE GERÇEK ZAMANLI KIŞISEL KORUYUCU EKIPMAN VE DEPO GÜVENLIĞI TESPITI. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 11 24 402–414.

IEEE

[1]A. Karakan and Y. Oğuz, “DERIN ÖĞRENME TABANLI İŞYERI KAMERASI ILE GERÇEK ZAMANLI KIŞISEL KORUYUCU EKIPMAN VE DEPO GÜVENLIĞI TESPITI”, Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 11, no. 24, pp. 402–414, Dec. 2024, doi: 10.54365/adyumbd.1470598.

ISNAD

Karakan, Abdil - Oğuz, Yüksel. “DERIN ÖĞRENME TABANLI İŞYERI KAMERASI ILE GERÇEK ZAMANLI KIŞISEL KORUYUCU EKIPMAN VE DEPO GÜVENLIĞI TESPITI”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 11/24 (December 1, 2024): 402-414. https://doi.org/10.54365/adyumbd.1470598.

JAMA

1.Karakan A, Oğuz Y. DERIN ÖĞRENME TABANLI İŞYERI KAMERASI ILE GERÇEK ZAMANLI KIŞISEL KORUYUCU EKIPMAN VE DEPO GÜVENLIĞI TESPITI. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2024;11:402–414.

MLA

Karakan, Abdil, and Yüksel Oğuz. “DERIN ÖĞRENME TABANLI İŞYERI KAMERASI ILE GERÇEK ZAMANLI KIŞISEL KORUYUCU EKIPMAN VE DEPO GÜVENLIĞI TESPITI”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 11, no. 24, Dec. 2024, pp. 402-14, doi:10.54365/adyumbd.1470598.

Vancouver

1.Abdil Karakan, Yüksel Oğuz. DERIN ÖĞRENME TABANLI İŞYERI KAMERASI ILE GERÇEK ZAMANLI KIŞISEL KORUYUCU EKIPMAN VE DEPO GÜVENLIĞI TESPITI. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2024 Dec. 1;11(24):402-14. doi:10.54365/adyumbd.1470598

DERIN ÖĞRENME TABANLI İŞYERI KAMERASI ILE GERÇEK ZAMANLI KIŞISEL KORUYUCU EKIPMAN VE DEPO GÜVENLIĞI TESPITI

Öz

Anahtar Kelimeler

REAL-TIME PERSONAL PROTECTIVE EQUIPMENT AND WAREHOUSE SAFETY DETECTION WITH DEEP LEARNING-BASED WORKPLACE CAMERA

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Early Pub Date

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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