An Adaptive Hybrid Attention Based Convolutional Neural Net for Intelligent Transportation Object Recognition

The rapid development of communication transmission, including 6G technology, is creating increasing challenges for real-world object recognition tasks in transportation, which now must operate within complex external environments and the requirement of time efficiency. Although machine learning-bas...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2023-07, Vol.24 (7), p.7791-7801
Main Authors: Chen, Qili, Pan, Guangyuan, Zhao, Lin, Fan, Junfang, Chen, Wenbai, Zhang, Ancai
Format: Article
Language:English
Subjects:
Adaptation models
Algorithms
Artificial neural networks
Brain modeling
Cognition
Condition monitoring
Deep learning
Entropy
Feature extraction
hybrid intelligent system
image recognition
Image segmentation
Machine learning
Mathematical models
Maximum entropy
Object recognition
Road conditions
Signal communication
Task complexity
Traffic signs
Training
Transportation
Vehicle identification
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Summary:The rapid development of communication transmission, including 6G technology, is creating increasing challenges for real-world object recognition tasks in transportation, which now must operate within complex external environments and the requirement of time efficiency. Although machine learning-based hybrid intelligence has attracted significant attention and achieved much success in recent years, the current models are often ineffective and have poor generalization in extreme weather. This is because the training of a deep learning model is often uncourteous, meaning that the models can easily fail, even during the feature extraction step. An adaptive hybrid attention-based convolutional neural network (AHA-CNN) framework is proposed in this paper to address these shortcomings. First, fuzzy c-means and maximum entropy algorithms are utilized for image feature pre-extraction. A heuristic search-based adaptive attention mechanism is then presented, which adaptively combines the previously extracted features and generates fused images. By applying this mechanism, the key areas of an image are reinforced in a more intelligent and interpretable way, and less important areas are ignored. The processed images are then transferred into a modified region-CNN for further training. Finally, four real-world experiments on traffic sign detection, vehicle license plate recognition, road surface condition monitoring, and pavement disease detection are carried out. Results show that the proposed framework has high testing accuracy compared with other existing methods. The features fused with the cognition mechanism are also easier to interpret.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3227245