Deep Learning Models Integrating Attention Mechanisms For Military Camouflaged Object Detection

Abstract

: The Adaptive Camouflage Data Set (ACD1K) is a carefully curated collection of high-quality images developed to facilitate research in camouflage detection and segmentation tasks. This dataset is categorized into training, validation, and testing subsets, enabling comprehensive evaluation of deep learning models. Models including Attention U-Net, built upon the ResNet-50 architecture, and U-Net++, enhanced with attention mechanisms, were employed for robust feature extraction. Performance evaluation was carried out using common metrics such as accuracy, precision, recall, F1-score, and intersection over union. The Attention U-Net model, in conjunction with CLAHE preprocessing, Adamax optimizer, a learning rate of 1e-5, and a dropout rate of 0.2, achieved an accuracy of 96.88% and an intersection over union of 92.01%. Under similar experimental conditions, the Attention U-Net++ model using the Adam optimizer achieved an accuracy of 98.32% and an intersection over union of 82.09%. These findings highlight the effectiveness of CNN-based architectures in accurately identifying camouflaged objects within visually complex environments.

Keywords

• Attention Mechanisms
• Deep Learning
• Image Segmentation
• Object Detection
• Adaptive Camouflage Dataset

Kamufle Askerî Nesnelerin Tespiti İçin Dikkat Mekanizmalarıyla Entegre Edilmiş Derin Öğrenme Modelleri

Abstract

Adaptive Camouflage Veri Seti (ACD1K), kamuflaj tespiti ve segmentasyon görevlerine yönelik araştırmaları desteklemek amacıyla özenle derlenmiş, yüksek kaliteli görsellerden oluşan bir koleksiyondur. Bu veri seti, derin öğrenme modellerinin kapsamlı bir şekilde değerlendirilmesine olanak tanımak üzere eğitim, doğrulama ve test alt kümelerine ayrılmıştır. Özellikle ResNet-50 mimarisi üzerine inşa edilmiş Attention U-Net modeli ile dikkat mekanizmalarıyla geliştirilen U-Net++ modeli, güçlü özellik çıkarımı amacıyla kullanılmıştır. Modellerin performansı; doğruluk (accuracy), kesinlik (precision), duyarlılık (recall), F1-skoru ve IoU (intersection over union) gibi yaygın metrikler kullanılarak değerlendirilmiştir. CLAHE önişleme yöntemi, Adamax optimizasyon algoritması, 1e-5 öğrenme oranı ve %20 dropout oranı ile birlikte kullanılan Attention U-Net modeli, %96.88 doğruluk ve %92.01 IoU değeri elde etmiştir. Benzer deneysel koşullar altında, Adam optimizasyon algoritmasıyla eğitilen Attention U-Net++ modeli ise %98.32 doğruluk ve %82.09 IoU performansı göstermiştir. Bu bulgular, görsel olarak karmaşık ortamlarda kamufle edilmiş nesnelerin doğru şekilde tespiti açısından, CNN tabanlı mimarilerin etkinliğini açıkça ortaya koymaktadır.

Keywords

• Dikkat Mekanizmaları
• Derin Öğrenme
• Görüntü Bölütleme
• Nesne Tespiti
• Uyarlanabilir Kamuflaj Veri Kümesi

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