用密集池化提升人形检测，效果优于当前主流方法。
Improving Detection of Person Class Using Dense Pooling
- 引入密集池化将图像转为3D模型再生成UV图，增强特征提取。
- 在COCO数据集6982张含人图像上测试，检测性能显著提升。
- 适合关注目标检测与图像特征优化的研究者参考。
近年来，计算机视觉领域深度学习模型的持续发展吸引了更多研究者致力于提升模型精度。FasterRCNN[32]已在COCO数据集上实现了对80类物体检测的最先进水平。为进一步提升人形检测性能，本文提出一种新方法：在FasterRCNN的ROI阶段，采用密集池化将图像转换为3D模型，并生成紫外（UV）图像，以更有效地提取特征。实验基于COCO数据集，选取6982张包含人形的目标图像进行验证，结果表明该方法在人形检测任务上取得了显著改进，达到当前最优性能。
原文摘要 · Abstract (English)
Lately, the continuous development of deep learning models by many researchers in the area of computer vision has attracted more researchers to further improve the accuracy of these models. FasterRCNN [32] has already provided a state-of-the-art approach to improve the accuracy and detection of 80 different objects given in the COCO dataset. To further improve the performance of person detection we have conducted a different approach which gives the state-of-the-art conclusion. An ROI is a step in FasterRCNN that extract the features from the given image with a fixed size and transfer into for further classification. To enhance the ROI performance, we have conducted an approach that implements dense pooling and converts the image into a 3D model to further transform into UV(ultra Violet) images which makes it easy to extract the right features from the images. To implement our approach we have approached the state-of-the-art COCO datasets and extracted 6982 images that include a person object and our final achievements conclude that using our approach has made significant results in detecting the person object in the given image
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