Box-level Segmentation Supervised Deep Neural Networks for Multispectral Pedestrian Detection(19)综述
摘要：本文提出了一种新颖的在检测框层面做语义分割的监督学习框架，能够实现准确且实时地多光谱（可见光和红外光）行人检测。具体来说就是将已经获得的行人检测框的普通图像和红外图像(已对齐)作为输入，然后估计出准确的预测热力图来表征行人的存在。 比之前的用anchor来做的多光谱检测方法，本文方法有两个有点： 1） 抛弃anchor的方法，避免了复杂的anchor超参数设置，而且提高了准确率，对小目标和遮
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相关文章
- 1. 《Constrained Convolutional Neural Networks for Weakly Supervised Segmentation》翻译
- 2. Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks （深度学习多光谱行人检测综述）
- 3. Brain tumor segmentation with Deep Neural Networks
- 4. 【Semantic Segmentation】语义分割综述 - - Semi-supervised
- 5. Aggregated Residual Transformations for Deep Neural Networks
- 6. Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts
- 7. Deep Neural Networks for Object Detection
- 8. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 9. ROLO:Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking
- 10. Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection