Improving Object Recognition Accuracy through CNN-based Localization Techniques

Convolutional Neural Networks (CNNs) and deep learning are responsible for the quick advancement of computer vision. A crucial task in computer vision is object localization, which involves identifying and finding objects within images. In this study, we propose an object localization method using a TensorFlow CNN model. In the proposed method, the model is trained on a specific dataset of images containing emojis with annotations for object locations and using it to predict the locations of emojis in test images. The performance of the proposed model is evaluated on a validation set, achieving a mean intersection over union (IoU) score of 0.85, indicating high accuracy in detecting emoji locations. The proposed results show that the proposed TensorFlow CNN model can effectively localize emojis in images, which can have practical applications in fields such as social media analysis and emoji-based communication. The proposed method can also be extended to localize multiple emojis in images by modifying the model architecture and training on a larger dataset.