Lastik Kusurlarının Tespiti için Yapay Zeka Destekli Yeni Bir Hibrit Model: CTLDF+EnC

Yıl 2024, Cilt: 17 Sayı: 3, 231 - 242, 31.07.2024

Özcan Askar , Ramazan Tekin

https://doi.org/10.17671/gazibtd.1465294

Öz

Bu çalışma, araç sürücülerinin lastiklerindeki çatlakları tespit etmek için önerilen yapay zeka tabanlı bir aşınmış lastik tespit sistemine odaklanmaktadır. Sürücüler genellikle lastik diş derinliği ve hava basıncının öneminin farkında olsalar da, lastik oksidasyonu ile ilişkili risklerin farkında değillerdir. Ancak, lastik oksidasyonu ve çatlakları sürüş güvenliğini etkileyen önemli sorunlara neden olabilir. Bu makalede, lastik çatlağı tespiti için, önceden eğitilmiş transfer öğrenme yöntemlerinden elde edilen derin özellikleri topluluk öğrenme yöntemleriyle birleştirerek kullanan yeni bir hibrit mimari olan CTLDF+EnC (Basamaklandırılmış Transfer Öğrenme Derin Özellikler + Ensemble Sınıflandırıcılar) önerilmektedir. Önerilen hibrit model, dokuz transfer öğrenme yönteminden gelen özellikleri ve İstifleme, Yumuşak ve Katı oylama topluluk öğrenme yöntemlerini içeren sınıflandırıcıları kullanmaktadır. Endüstriyel kullanıma yönelik X-Ray görüntü tabanlı uygulamalardan farklı olarak bu çalışmada önerilen model herhangi bir dijital görüntüleme cihazından elde edilen görüntülerle çalışabilmektedir. Çalışmada önerilen modeller arasında en yüksek test doğruluk değeri %76,92 olarak CTLDF+EnC ( İstifleme) hibrit modeli ile elde edilmiştir. CTLDF+EnC ( Yumuşak) ve CTLDF+EnC (Katı) modelleri ile sırasıyla %74,15 ve %72,92 doğruluk değerleri elde edilmiştir. Çalışmanın sonuçları, önerilen hibrit modellerin lastik sorunlarını tespit etmede etkili olduğunu göstermektedir. Ayrıca, düşük maliyetli ve uygulanabilir bir yapı sunulmuştur.

Anahtar Kelimeler

Transfer Öğrenme, Derin Öznitelikler, Topluluk Öğrenme, Lastik Çatlakları

A New Hybrid Model for Artificial Intelligence Assisted Tire Defect Detection: CTLDF+EnC

Öz

This paper focuses on an artificial intelligence based worn tire detection system proposed to detect cracks in the tires of vehicle drivers. Although drivers are generally aware of the importance of tire tread depth and air pressure, they are not aware of the risks associated with tire oxidation. However, tire oxidation and cracks can cause significant problems affecting driving safety. In this paper, we propose a new hybrid architecture for tire crack detection, CTLDF+EnC (Cascaded Transfer Learning Deep Features + Ensemble Classifiers), which uses deep features from pre-trained transfer learning methods in combination with ensemble learning methods. The proposed hybrid model utilizes features from nine transfer learning methods and classifiers including Stacking, Soft and Hard voting ensemble learning methods. Unlike X-Ray image-based applications for industrial use, the model proposed in this study can work with images obtained from any digital imaging device. Among the models proposed in the study, the highest test accuracy value was obtained as 76.92% with the CTLDF+EnC (Stacking) hybrid model. With CTLDF+EnC (Soft) and CTLDF+EnC (Solid) models, 74.15% and 72.92% accuracy values were obtained respectively. The results of the study show that the proposed hybrid models are effective in detecting tire problems. In addition, a low-cost and feasible structure is presented.

Anahtar Kelimeler

Transfer Learning, Deep Features, Ensemble Learning, Tire Cracks

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