Recognition of Safety Helmet Wearing Of Operator Local Ground Floor Unit 2 Suralaya PGU Based On Improved YOLOv3

Firlan Maulana Ruaz(1*)

(1) PT. PLN Indonesia Power, Suralaya Power Generation Unit, Suralaya, Indonesia
(*) Corresponding Author


PT. PLN Indonesia Power Suralaya Power Generation Unit requires its operators on the local ground floor to wear safety helmets to prevent head injuries while performing their tasks. Observation of workers wearing safety helmet must be ensured by the company to prevent head injury by operators. The Culture Transformation Program of K3 PT PLN Indonesia Power brings a new observation method to detect safety helmet wearing of operator local ground floor using real-time detection. YOLOv3 is an application which can be used to real-time detection helmet wearing of workers using the darknet53 algorithm based on YOLOv1 and YOLOv2 by using data images that have been collected. Based on YOLOv3 model, the improved version of YOLOv3 is proposed to improve accuracy and speed detection of safety helmet wearing by combining multi-scale detection training. Different YOLOv3 versions will be used to compare results of helmet recognition. The improved YOLOv3 show results 96.63% mAP50 and 725,711 milliseconds better than the other versions of YOLO to detect helmet safety. The experimental result show that the improved YOLOv3 have satisfying with the detection speed and accuracy of safety helmet wearing detection by operators at PT PLN Indonesia Power Suralaya Power Generation Unit.


YOLO; safety helmet; deep learning; real-time detection; deep residual network.

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