Helmet detectionon the construction site with transfer learning and without transfer learning deep networks

Year 2023, , 39 - 51, 15.01.2023

Mehmet Uğur Türkdamar , Murat Taşyürek , Celal Öztürk

https://doi.org/10.28948/ngumuh.1173944

Abstract

The widespread use of systems that prioritize human life provides holistic benefits to societies. In order to avoid respiratory contagious diseases, wearing a mouth-nose mask has become mandatory with the Covid-19 pandemic, and workers working in building construction are required to wear a head helmet at the construction site. It is tiring and error-prone to visually check whether the workers working on the construction sites are wearing their helmets. In this age, where artificial intelligence-based computer technologies are developed, the existence of systems that make our lives easier in every sense is promising. In this study, it is proposed to make helmet wearing control automatic with convolutional neural network (CNN) based deep learning in which the image data is meaningful. The limited data set problem was overcome with the transfer learning technique applied to the YOLO V4, V5 and Faster R-CNN models. The effectiveness of the method was examined by including the trainings in which transfer learning was not applied in the experiments. As a result, it was observed that the YOLO V5 model with transfer learning was the most successful among 6 different model trainings with an f1 score of 98%.

Keywords

Helmet detection, Deep learning, Transfer learning, CNN, Artificial intelligence

Transfer öğrenmeli ve transfer öğrenmesiz derin ağlar ile inşaat alanında kask tespiti

Abstract

İnsan yaşamını önceleyen sistemlerin yaygınlaşması toplumlara bütüncül fayda sağlamaktadır. Solunum yoluyla bulaşıcı hastalıklardan sakınmak için ağız-burun maskesi takmanın Covid-19 pandemisi ile zorunlu hâle geldiği gibi yapı inşaatında çalışan işçilerin inşaat alanında kafa kaskı takması zorunludur. İnşat alanlarında çalışan işçilerin kaskını takıp takmadığının kontrolünü göz ile yapmak yorucu ve hataya açıktır. Yapay zekâ tabanlı bilgisayar teknolojilerinin geliştiği bu çağda hayatımızı her anlamda kolaylaştıran sistemlerin varlığı ümit vaat etmektedir. Bu çalışmada görüntü verisinin anlamlandığı evrişimli sinir ağı (ESA) tabanlı derin öğrenme ile kask takma kontrolünün otomatik yapılması önerilmiştir ve YOLO V4, V5 ve Faster R-CNN modellerine uygulanan transfer öğrenme tekniği ile kısıtlı veri seti probleminin üstesinden gelinmiştir. Deneylerde transfer öğrenme uygulanmayan eğitimlere de yer verilerek yöntemin etkinliği incelenmiştir. Sonuçta transfer öğrenmeli YOLO V5 modelinin %98 f1 skor ile 6 farklı model eğitimi arasında en başarılı olduğu gözlemlenmiştir.

Keywords

Kask Tespiti, Derin Öğrenme, Transfer Öğrenme, ESA, Yapay Zekâ, Helmet detection, Deep learning, Transfer learning, CNN, Artificial intelligence

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