Enhancing the identification accuracy of deep learning object detection using natural language processing

In recent years, object detection technology with artificial intelligence has been applied in many fields. This study uses a deep learning method to train an identification model to classify and browse pictures of the 600 different kinds of birds in Taiwan. To enhance the accuracy of identification and classification of these birds, we propose an automatic extraction system that can obtain training data by visiting public social media pages. We also develop mobile apps that allow users to take pictures of birds and upload them to an identification server to enable real-time identification and provide training data. These mobile apps are sent candidate bird pictures by the identification server to allow users to confirm and give feedback when the confidence level of identification is within a critical range. The bird pictures are then used as training data, and the identification model is periodically retrained to optimise the model. We also use natural language processing technology to enhance the level of confidence in image identification. The features of the birds’ appearance are described in words and candidate birds are obtained through image identification and used to readjust the adopted weight values. The proposed identification system gives a relatively high identification accuracy due to the use of deep learning object detection.