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Multi-scale traffic sign detection model with attention
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering ( IF 1.7 ) Pub Date : 2020-08-27 , DOI: 10.1177/0954407020950054 Bei Bei Fan 1 , He Yang 1
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering ( IF 1.7 ) Pub Date : 2020-08-27 , DOI: 10.1177/0954407020950054 Bei Bei Fan 1 , He Yang 1
Affiliation
The current traffic sign detection technology is disturbed by factors such as illumination changes, weather, and camera angle, which makes it unsatisfactory for traffic sign detection. The traffic sign data set usually contains a large number of small objects, and the scale variance of the object is a huge challenge for traffic indication detection. In response to the above problems, a multi-scale traffic sign detection algorithm based on attention mechanism is proposed. The attention mechanism is composed of channel attention mechanism and spatial attention mechanism. By filtering the background information on redundant contradictions with channel attention mechanism in the network, the information on the network is more accurate, and the performance of the network to recognize the traffic signs is improved. Using spatial attention mechanism, the proposed method pays more attention to the object area in traffic recognition image and suppresses the non-object area or background areas. The model in this paper is validated on the Tsinghua-Tencent 100K data set, and the accuracy of the experiment reached a higher level compared to state-of-the-art approaches in traffic sign detection.
中文翻译:
具有注意力的多尺度交通标志检测模型
目前的交通标志检测技术受光照变化、天气、摄像机角度等因素的干扰,使得其对交通标志检测效果不尽如人意。交通标志数据集通常包含大量的小物体,物体的尺度方差对交通标志检测是一个巨大的挑战。针对上述问题,提出了一种基于注意力机制的多尺度交通标志检测算法。注意力机制由通道注意力机制和空间注意力机制组成。通过网络中的通道注意力机制过滤冗余矛盾的背景信息,使网络上的信息更加准确,提高了网络对交通标志的识别性能。使用空间注意力机制,所提出的方法更加关注交通识别图像中的目标区域,并抑制了非目标区域或背景区域。本文模型在清华-腾讯100K数据集上进行了验证,与交通标志检测的最新方法相比,实验的准确性达到了更高的水平。
更新日期:2020-08-27
中文翻译:
具有注意力的多尺度交通标志检测模型
目前的交通标志检测技术受光照变化、天气、摄像机角度等因素的干扰,使得其对交通标志检测效果不尽如人意。交通标志数据集通常包含大量的小物体,物体的尺度方差对交通标志检测是一个巨大的挑战。针对上述问题,提出了一种基于注意力机制的多尺度交通标志检测算法。注意力机制由通道注意力机制和空间注意力机制组成。通过网络中的通道注意力机制过滤冗余矛盾的背景信息,使网络上的信息更加准确,提高了网络对交通标志的识别性能。使用空间注意力机制,所提出的方法更加关注交通识别图像中的目标区域,并抑制了非目标区域或背景区域。本文模型在清华-腾讯100K数据集上进行了验证,与交通标志检测的最新方法相比,实验的准确性达到了更高的水平。