Today, with the increasing number of criminal activities, automatic control systems are becoming the primary need for security forces. In this study, a new model is proposed to detect seven different weapon types using the deep learning method. This model offers a new approach to weapon classification based on the VGGNet architecture. The model is taught how to recognize assault rifles, bazookas, grenades, hunting rifles, knives, pistols, and revolvers. The proposed model is developed using the Keras library on the TensorFlow base. A new model is used to determine the method required to train, create layers, implement the training process, save training in the computer environment, determine the success rate of the training, and test the trained model. In order to train the model network proposed in this study, a new dataset consisting of seven different weapon types is constructed. Using this dataset, the proposed model is compared with the VGG-16, ResNet-50, and ResNet-101 models to determine which provides the best classification results. As a result of the comparison, the proposed model’s success accuracy of 98.40% is shown to be higher than the VGG-16 model with 89.75% success accuracy, the ResNet-50 model with 93.70% success accuracy, and the ResNet-101 model with 83.33% success accuracy.

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ÖzGünümüz toplumunda, insanları tehdit eden en önemli etmenlerden birisi terörizmdir. Terörizm bir toplumda, insanların düzen durumlarını bozarak, yaşam kalitesini etkilemektedir. Devletler ise terörle mücadele etmek için sürekli farklı yöntemler geliştirmektedir. Bu yöntemlerden birisi de terörle mücadele için makine öğrenmesinin bir alt alanı olan derin öğrenmenin kullanılmasıdır. Derin öğrenme, makine öğrenmesi alanında son yıllarda oldukça popülerlik kazanmıştır. Bu çalışmada, terör faaliyetlerini fark etmek ve önlemek için derin öğrenmeye dayalı VGG-16 mimarisi temel alınarak yeni bir model önerilmektedir. Önerilen model ile güvenlik kontrollerinde kullanılan kamera görüntülerinden alınan görüntülerde, insan ya da tren rayları üzerinde dinamit tespit edildiğinde, durumu hızla belirlemek ve uygun önlemleri almak için güvenlik görevlilerini uyaran bir sistem gerçekleştirilmiştir. Çalışmada kullanılan veri seti, internet ortamından indirilen dinamit resimleri düzenlenerek oluşturulmuştur. Önerilen modelin performansını değerlendirmek için, insan ya da tren rayları üzerinde bulunan dinamit resimleri test edilerek, %98,4'lük başarı doğruluğu ve 0,024 kayıp oranıyla dinamit görüntüleri tespit edilmektedir.

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Fish remains popular among the body’s most essential nutrients, as it contains protein and polyunsaturated fatty acids. It is extremely important to choose the fish consumption according to the season and the freshness of the fish to be purchased. It is very difficult to distinguish between non-fresh fish and fresh fish mixed in the fish stalls. In addition to traditional methods used to determine meat freshness, significant success has been achieved in studies on fresh fish detection with artificial intelligence techniques. In this study, two different types of fish (anchovy and horse mackerel) used to determine fish freshness with convolutional neural networks, one of the artificial intelligence techniques. The images of fresh fish were taken, images of non-fresh fish were taken and two new datasets (Dataset1: Anchovy, Dataset2: Horse mackerel) were created. A novel hybrid model structure has been proposed to determine fish freshness using fish eye and gill regions on these two datasets. In the proposed model, Yolo-v5 and Inception-ResNet-v2 and Xception model structures are used through transfer learning. Whether the fish is fresh in both of the Yolo-v5 + Inception-ResNet-v2 (Dataset1: 97.67%, Dataset2: 96.0%) and Yolo-v5 + Xception (Dataset1: 88.00%, Dataset2: 94.67%) hybrid models created using these model structures has been successfully detected. Thanks to the model we have proposed, it will make an important contribution to the studies that will be conducted in the freshness studies of fish using different storage days and the estimation of fish size.