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Shuang Hu

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Proceedings Article | 5 July 2024 Paper

Visual attention estimation and cascade region enhancement detection strategy for intelligent vehicles

Baixuan Zhao, Wangwang Zhu, Shuang Hu, Chuan Hu, Xi Zhang

Proceedings Volume 13184, 131842L (2024) https://doi.org/10.1117/12.3032842

KEYWORDS: Visualization, Object detection, Visual process modeling, Performance modeling, Strategic intelligence, Windows, Sensors, Process modeling, Image processing, Data modeling

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Vision-based object detection in intelligent vehicles presents unique challenges distinct from regular object detection tasks. It requires identifying a critical attention region that significantly influences the vehicle's driving behavior, determined by the ego-vehicle's driving state and the behavior of surrounding traffic participants. Traditional approaches process the entire image uniformly, leading to unnecessary computational overhead. This study introduces a novel approach to precisely and efficiently estimate the attention region, enhancing the detection of crucial objects within it and overall detection performance. Firstly, the method proposes an attention region estimation technique. This technique treats the computation of the attention region as calculating a mass center, considering the ego-vehicle's driving state and nearby traffic participants. Subsequently, the study proposes a cascade region enhancement detection strategy. Utilizing the estimated attention region, this strategy adaptively alternates between lightweight and heavyweight models for the detection tasks, optimizing performance and resource usage. To validate the proposed method's effectiveness, a real-vehicle dataset is compiled, featuring critical objects annotated by drivers. Comparative results demonstrate that the proposed approach significantly boosts efficiency while maintaining high accuracy in detecting critical objects, surpassing the performance of baseline models.

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