计算机科学
人工智能
卷积神经网络
智能交通系统
特征提取
目标检测
深度学习
机器学习
人工神经网络
强化学习
特征(语言学)
模式识别(心理学)
工程类
语言学
哲学
土木工程
作者
Qili Chen,Guangyuan Pan,Lin Zhao,Junfang Fan,Wenbai Chen,Ancai Zhang
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2023-07-01
卷期号:24 (7): 7791-7801
被引量:1
标识
DOI:10.1109/tits.2022.3227245
摘要
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.
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