Hand-Gun Detection in Images with Transfer Learning-Based Convolutional Neural Networks

This study aims to evaluate the performance of different deep learning methods based on CNN (Convolutional Neural Networks) for hand-gun detection from images. Within the context of object detection, which is an application of CNN, this study further focusses on hand-gun detection from images. The application of a CNN trained from scratch, transfer learning method using VGG-16 (Visual Geometry Group) model and fine-tuning based on the same model are applied on the same image dataset and the results are evaluated using various evaluation metrics. The results on 8300 images with and without a hand-gun showed that the highest accuracies are obtained with fine tuning method. Besides, it has been observed that the CNN model obtained with fine-tuning method gave balanced accuracies on the images containing and not containing hand-gun.