计算机科学
人工智能
突出
目标检测
特征(语言学)
水准点(测量)
计算机视觉
可视化
高级驾驶员辅助系统
智能交通系统
融合
模式识别(心理学)
工程类
哲学
语言学
土木工程
大地测量学
地理
作者
Ning Jia,Yougang Sun,Xianhui Liu
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-11
被引量:1
标识
DOI:10.1109/tits.2023.3293822
摘要
Emergency prediction and driver attention prediction are fundamental tasks within the realm of self-driving vehicles and assistant driving systems. The utilization of visual saliency detection in these tasks has garnered considerable attention, owing to its inherent advantages. However, current research on traffic saliency detection primarily focuses on emulating the human visual system for attention allocation in traffic scenes, neglecting the detection of complete salient objects. In this paper, we propose the Traffic Salient Object Detection Using a Feature Deep Interaction and Guidance Fusion Network (TFGNet). Different from previous methods, our method detects the complete objects that attract human attention in natural traffic scenes, rather than a certain point without object semantic information, which can provide assistance for target recognition tasks in the domain of intelligent driving. Moreover, we propose a traffic salient object detection framework based on feature interaction and guidance fusion, enabling the detection of salient objects across varying scales. Experimental results on multiple benchmark datasets demonstrate that, compared to the state-of-the-art methods, our method exhibits superior performance in terms of precision, recall, and error rate.
科研通智能强力驱动
Strongly Powered by AbleSci AI