Web-based Timber Logs Information System Using the YOLOv8 Model: IstifTakip

Year 2024, Volume: 20 Issue: 2, 370 - 394, 28.12.2024

Remzi Eker , Kamber Can Alkiş

https://doi.org/10.58816/duzceod.1573955

Abstract

This study introduces İstifTakip, a web-based information system developed for the automated detection and measurement of stacked timber logs using the YOLOv8 deep learning model. The system aims to overcome the limitations of manual timber measurement methods by providing a more accurate and efficient alternative. Data were collected through smartphone images of timber stacks at the Ulucak Forest Depot in İzmir, Türkiye. The YOLOv8 model, optimized using the Optuna library, was trained on this dataset to detect logs and calculate key attributes such as diameter and volume. Hyperparameter optimization with Optuna resulted in a significant improvement in model performance, achieving an mAP@0.5 score of 0.8569, precision of 0.8513, and recall of 0.8827. These results demonstrate the model’s robustness and accuracy in detecting logs across varied image conditions. İstifTakip was developed using the Django framework and offers a user-friendly interface where users can upload images, annotate reference lines, and obtain log measurements. The system is specifically designed for Turkish forestry, supporting local language and practices, which sets it apart from other global solutions. Its scalability and potential for integration with mobile devices make it a valuable tool for future forestry applications. This research highlights the advantages of combining deep learning and smart forestry technologies to enhance operational efficiency and data accuracy in timber stack management.

Keywords

Deep learning, Smart forestry, Timber detection, YOLOv8

YOLOv8 Modeli ile Web Tabanlı Tomruk Bilgi Sistemi: İstifTakip

Abstract

Bu çalışma, YOLOv8 derin öğrenme modeli kullanarak istiflenmiş tomrukların otomatik tespit ve ölçümünü sağlayan web tabanlı bir sistem olan İstifTakip'i tanıtmaktadır. Sistem, manuel tomruk ölçüm yöntemlerinin sınırlamalarını aşarak daha doğru ve verimli bir çözüm sunmayı hedeflemektedir. Veriler, Türkiye’nin İzmir ilindeki Ulucak Orman Deposu’nda tomruk istiflerinin akıllı telefonla çekilen görüntülerinden toplanmıştır. Bu görüntülerle çap ve hacim gibi özellikleri hesaplamak amacıyla YOLOv8 modeli Optuna kütüphanesi ile optimize edilmiştir. Optuna ile yapılan hiperparametre optimizasyonu, model performansını artırarak mAP@0.5 skoru 0.8569, doğruluk 0.8513 ve geri çağırma 0.8827 seviyelerine ulaşmıştır. Bu sonuçlar, modelin değişen görüntü koşullarında sağlam ve başarılı olduğunu göstermektedir. Django framework kullanılarak geliştirilen İstifTakip, kullanıcı dostu bir arayüze sahiptir. Kullanıcılar, bu arayüzde görüntü yükleyebilir, referans çizgileri ekleyebilir ve tomruk ölçümleri alabilir. Türk ormancılığına özel olarak tasarlanan sistem, yerel dil desteği ve uygulamalarıyla öne çıkmakta olup, küresel çözümlerden ayrılmaktadır. Ayrıca, mobil cihazlarla entegrasyon potansiyeli ve ölçeklenebilirliği ile gelecekteki ormancılık uygulamaları için değerli bir araç niteliğindedir. Bu araştırma, derin öğrenme ve akıllı ormancılık teknolojilerinin bir araya gelerek istif yönetiminde operasyonel verimlilik ve veri doğruluğunu artırmadaki avantajlarını vurgulamaktadır.

Keywords

Derin öğrenme, Akıllı ormancılık, Tomruk tespit, YOLOv8

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