Improved Vehicle Detection Using Weather Classification and Faster R-CNN with Dark Channel Prior

Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Study

Object Detection in Video using Faster R-CNN

Convolutional neural networks (CNN) currently dominate the computer vision landscape. Recently, a CNN based model, Faster R-CNN [1], achieved stateof-the-art performance at object detection on the PASCAL VOC 2007 and 2012 datasets. It combines region proposal generation with object detection on a single frame in less than 200ms. We apply the Faster R-CNN model to video clips from the ImageNet 2...

Symbol detection in online handwritten graphics using Faster R-CNN

Symbol detection techniques in online handwritten graphics (e.g. diagrams and mathematical expressions) consist of methods specifically designed for a single graphic type. In this work, we evaluate the Faster R-CNN object detection algorithm as a general method for detection of symbols in handwritten graphics. We evaluate different configurations of the Faster R-CNN method, and point out issues...

Mammography Lesion Detection Using Faster R-cnn Detector

Recently availability of large scale mammography databases enable researchers to evaluates advanced tumor detections applying deep convolution networks (DCN) to mammography images which is one of the common used imaging modalities for early breast cancer. With the recent advance of deep learning, the performance of tumor detection has been developed by a great extent, especially using R-CNNs or...

Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection

Multispectral images of color-thermal pairs have shown more effective than a single color channel for pedestrian detection, especially under challenging illumination conditions. However, there is still a lack of studies on how to fuse the two modalities effectively. In this paper, we deeply compare six different convolutional network fusion architectures and analyse their adaptations, enabling ...