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Accurate and efficient traffic participant detection based on optimized features and multi-scale localization confidence
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering ( IF 1.7 ) Pub Date : 2022-08-24 , DOI: 10.1177/09544070221119282
Yue Liu, Ying Shi, Changjun Xie, Wenguang Luo, Hongtao Ye, Chaojun Lin

Traffic participant detection, which locates targets participating in road traffic such as vehicles and humans, is a critical research goal in autonomous driving and intelligent transportation technology, in which targets of various scales are challenging to handle. To address this problem, we propose an accurate and efficient traffic participant detector called FM-RepPoints to increase detection precision of multi-scale targets via optimized features and localization confidence. We first present a parameter-efficient backbone Dilated-ResNeSt to extract complicated urban scene features with less inference time. An adaptive attention module is then added to calibrate the features according to the corresponding targets. Moreover, we set the multi-scale localization confidence for traffic participants of various scales based on the sensitivity analysis. The proposed network outperforms the basic network by 3.3 points mAP; the accuracy of vehicles and humans is improved by 4.5% and 2.1%, respectively. Compared with state-of-the-art algorithms, the proposed method achieves the highest accuracy while maintaining average inference speed, thereby accomplishing the best speed-accuracy trade-off.



中文翻译:

基于优化特征和多尺度定位置信度的准确高效的交通参与者检测

交通参与者检测是对参与道路交通的车辆和人等目标进行定位,是自动驾驶和智能交通技术的一个关键研究目标,其中各种规模的目标都具有挑战性。为了解决这个问题,我们提出了一种准确高效的交通参与者检测器,称为 FM-RepPoints,通过优化特征和定位置信度来提高多尺度目标的检测精度。我们首先提出了一种参数有效的骨干 Dilated-ResNeSt,以更短的推理时间提取复杂的城市场景特征。然后添加一个自适应注意模块以根据相应的目标校准特征。而且,我们基于敏感性分析为不同尺度的交通参与者设置了多尺度定位置信度。提出的网络比基础网络高出 3.3 点 mAP;车辆和人的准确率分别提高了 4.5% 和 2.1%。与最先进的算法相比,所提出的方法在保持平均推理速度的同时实现了最高的准确度,从而实现了最佳的速度-准确度权衡。

更新日期:2022-08-24
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