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An anchor-free detector and R-CNN integrated neural network architecture for environmental perception of urban roads
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering ( IF 1.7 ) Pub Date : 2021-03-22 , DOI: 10.1177/09544070211004466
Chaojun Lin 1 , Ying Shi 1 , Jian Zhang 1 , Changjun Xie 1 , Wei Chen 1 , Yue Chen 1
Affiliation  

Environmental perception of urban roads is a critical research goal in intelligent transportation technology and autonomous vehicles, and pedestrian location is key to many relevant algorithms. Because anchor-free detectors are faster and region-based convolutional neural networks have a higher accuracy in object detection and classification, we propose an integrated convolutional networking architecture combining an anchor-free detector with a region-based convolutional neural network in the environmental perception task. The proposed network achieves higher precision and increases inference speed by up to 30%. To acquire more accurate region boundaries than a coarse bounding box method, a semantic segmentation sub-network is adopted to predict an instance segmentation mask for each object, and more accurate segmentation results are obtained by using the Dice loss. Moreover, we present an assignment strategy using a modified feature pyramid structure and show that it improves mean average precision of pedestrian detection by 2% on average. Finally, we verify that the pretrained neural network is beneficial for small datasets. Overall, the results show that our model achieves higher precision than the approaches used for comparison.



中文翻译:

用于城市道路环境感知的无锚检测器和R-CNN集成神经网络架构

城市道路的环境感知是智能交通技术和自动驾驶汽车的关键研究目标,行人位置是许多相关算法的关键。由于无锚检测器速度更快并且基于区域的卷积神经网络在对象检测和分类中具有更高的准确性,因此我们提出了一种集成的卷积网络体系结构,将无锚检测器与基于区域的卷积神经网络结合在一起用于环境感知任务。所提出的网络可以实现更高的精度,并且可以将推理速度提高多达30%。为了获得比粗边界框法更准确的区域边界,采用了语义分割子网来预测每个对象的实例分割掩码,通过使用Dice损失获得更准确的细分结果。此外,我们提出了一种使用改进的特征金字塔结构的分配策略,并表明该策略将行人检测的平均平均精度平均提高了2%。最后,我们验证了预训练的神经网络对于小型数据集是有益的。总体而言,结果表明,我们的模型比用于比较的方法具有更高的精度。

更新日期:2021-03-22
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