A maximum-entropy-attention-based convolutional neural network for image perception

In recent years, image perception such as enhancement, classification and object detection with deep learning has achieved significant successes. However, in real world under extreme conditions, the training of a deep learning model often yields low accuracy, low efficiency in feature extraction and generalizability, due to the inner uncourteous and uninterpretable characteristics. In this paper, a maximal-entropy-attention-based convolutional neural network (MEA-CNN) framework is proposed. A maximum entropy algorithm is first used for image feature pre-extraction. An attention mechanism is then proposed by combining the extracted features on original images. By applying the mechanism, the key areas of an image are enhanced, and noised area can be ignored. Afterward, the processed images are transferred into region convolutional neural network, which is a well-known pre-trained CNN model, for further feature learning and extraction. Finally, two real-world experiments on traffic sign recognition and road surface condition monitoring are designed. The results show that the proposed framework has high testing accuracy, with improvements of 17% and 2.9%, compared with some other existing methods. In addition, the features extracted by the model are more easily interpretable.

Automated summary

This paper proposes a maximal-entropy-attention-based convolutional neural network (MEA-CNN) framework that utilizes a maximum entropy algorithm for image feature pre-extraction and an attention mechanism to enhance key areas of an image. Experimental results show that the proposed framework achieves high testing accuracy in tasks such as traffic sign recognition and road surface condition monitoring, and the extracted features are more easily interpretable.