TFGNet: Traffic Salient Object Detection Using a Feature Deep Interaction and Guidance Fusion
Ning Jia, Yougang Sun, Xianhui Liu
Abstract
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.