雪
计算机科学
恶劣天气
气象学
点云
点(几何)
蒸馏
卷积(计算机科学)
对象(语法)
极端天气
环境科学
人工智能
模拟
人工神经网络
数学
气候变化
有机化学
化学
几何学
物理
生物
生态学
作者
Anh The Do,Myungsik Yoo
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:10: 84882-84893
被引量:2
标识
DOI:10.1109/access.2022.3197765
摘要
Recently, 3D object detection models have achieved very good performance under normal weather conditions, with the SE-SSD model having produced the highest performance by exchanging features between the teacher and student models. However, the performance of this model is significantly reduced by adverse weather conditions. Therefore, instead of training the teacher and student models simultaneously, we applied the knowledge distillation algorithm. In this algorithm, the teacher model is trained first by normal input, and the student model is then trained with distillation and student loss by adverse weather condition input. Although recent research has focused on combining different types of sensor inputs to enhance the original model’s performance in inclement weather, there are no studies that directly address the problem of missing points for point clouds. Accordingly, we applied a probability estimation, which includes a Deep Mixture of Factor Analyzers (DMFA) network and loss-convolution layer, to recover lost points. We conducted a model evaluation in both fog and snow environments at three levels of density - light, medium, and heavy - and compared the proposed model’s performance with that of two state-of-the-art models: one with normal weather condition, and the other with harsh weather conditions. Consequently, our proposed method was shown to significantly outperform the two existing models.
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