Query: Faster-RCNN

title: BubCNN: Bubble detection using Faster RCNN and shape regression network
abstract: Detailed knowledge about gas liquid multiphase flows is important to optimize industrial systems. Imaging with image processing is the most commonly used measurement technique. However, the workflow and parameters strongly depend on the experimental conditions and no generally applicable process has been developed yet. Here, a workflow based on convolutional neural networks (CNN) is proposed that can be used with a wider range of experimental conditions. The method, named BubCNN, employs a Faster region based CNN (RCNN) detector to locate bubbles and a shape regression CNN to predict bubble shape parameters. Hyperparameters and network architectures for both modules were systematically analyzed. BubCNN achieved accurate results for different experimental conditions. A pretrained program was made publicly available on GitHub. Since the whole variety of bubble images was not yet captured in the training data set, an additional semi automatic transfer learning module is provided that allows to customize BubCNN for different images.

title: Domain Adaptive Object Detection via Asymmetric Tri way Faster RCNN
abstract: Conventional object detection models inevitably encounter a performance drop as the domain disparity exists. Unsupervised domain adaptive object detection is proposed recently to reduce the disparity between domains, where the source domain is label rich while the target domain is label agnostic. The existing models follow a parameter shared siamese structure for adversarial domain alignment, which, however, easily leads to the collapse and out of control risk of the source domain and brings negative impact to feature adaption. The main reason is that the labeling unfairness (asymmetry) between source and target makes the parameter sharing mechanism unable to adapt. Therefore, in order to avoid the source domain collapse risk caused by parameter sharing, we propose an asymmetric tri way Faster RCNN (ATF) for domain adaptive object detection. Our ATF model has two distinct merits: 1) A ancillary net supervised by source label is deployed to learn ancillary target features and simultaneously preserve the discrimination of source domain, which enhances the structural discrimination (object classification vs. bounding box regression) of domain alignment. 2) The asymmetric structure consisting of a chief net and an independent ancillary net essentially overcomes the parameter sharing aroused source risk collapse. The adaption safety of the proposed ATF detector is guaranteed. Extensive experiments on a number of datasets, including Cityscapes, Foggy cityscapes, KITTI, Sim10k, Pascal VOC, Clipart and Watercolor, demonstrate the SOTA performance of our method.

title: Face Detection using Deep Learning: An Improved Faster RCNN Approach
abstract: In this paper, we present a new face detection scheme using deep learning and achieve the state of the art detection performance on the well known FDDB face detection benchmark evaluation. In particular, we improve the state of the art Faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi scale training, model pre training, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state of the art face detection performance and was ranked as one of the best models in terms of ROC curves of the published methods on the FDDB benchmark.

title: Multi Adversarial Faster RCNN for Unrestricted Object Detection
abstract: Conventional object detection methods essentially suppose that the training and testing data are collected from a restricted target domain with expensive labeling cost. For alleviating the problem of domain dependency and cumbersome labeling, this paper proposes to detect objects in unrestricted environment by leveraging domain knowledge trained from an auxiliary source domain with sufficient labels. Specifically, we propose a multi adversarial Faster RCNN (MAF) framework for unrestricted object detection, which inherently addresses domain disparity minimization for domain adaptation in feature representation. The paper merits are in three fold: 1) With the idea that object detectors often becomes domain incompatible when image distribution resulted domain disparity appears, we propose a hierarchical domain feature alignment module, in which multiple adversarial domain classifier submodules for layer wise domain feature confusion are designed; 2) An information invariant scale reduction module (SRM) for hierarchical feature map resizing is proposed for promoting the training efficiency of adversarial domain adaptation; 3) In order to improve the domain adaptability, the aggregated proposal features with detection results are feed into a proposed weighted gradient reversal layer (WGRL) for characterizing hard confused domain samples. We evaluate our MAF on unrestricted tasks including Cityscapes, KITTI, Sim10k, etc. and the experiments show the state of the art performance over the existing detectors.

title: Detection Approach Based on an Improved Faster RCNN for Brace Sleeve Screws in High Speed Railways
abstract: Brace sleeve screws are an important component of catenary support devices in high speed railways. According to the statistical data, there is a much risk for failure of brace sleeve screws during the operation of the catenary system. In order to obtain better results on the fault diagnosis for brace sleeve screws, it is of vital importance to develop a high precision detection approach for brace sleeve screws. With the aim of solving the problems that the size of the brace sleeve screw is too small and the employed previous methods are difficult to detect accurately, a novel object detection network based on an improved Faster RCNN is proposed. Through the analysis of the factors that influence the detection accuracy for brace sleeve screws and the characteristics of original Faster RCNN, the concepts of discrimination maps and proposal maps are introduced for the improved Faster RCNN. In order to retain the characteristic information of brace sleeve screws, the high resolution discrimination maps are adopted to identify the object type and the low resolution proposal maps are adopted to generate the candidate boxes of region with an appropriate anchor scale configuration. Experimental results show that the proposed method has a higher detection accuracy for brace sleeve screws. In addition, compared with original Faster RCNN implementation based on the VGG 16 and ResNet 101 backbone, the detection accuracy of the improved Faster RCNN is increased by 8% It also provides a new idea for general object detection of small targets.

title: A Downscaled Faster RCNN Framework for Signal Detection and Time Frequency Localization in Wideband RF Systems
abstract: We propose a wideband spectrum sensing technique to detect and localize wireless radio frequency (RF) signals of interest in time and frequency when uninteresting signals cause RF interference (RFI) Specifically, we adopt and downscale the existing Faster RCNN (FRCNN) framework to achieve better signal detection and localization than the state of the art. For experimental evaluation, we present a data generation framework for Wi Fi as the signals of interest and the Bluetooth and microwave oven signals as the RFI. Experiments reveal that (i) the downscaled FRCNN model can achieve up to a mean average precision (mAP) of 0.8, significantly outperforming the state of the art, (ii) feature extraction with the VGG 13 architecture gives the best mAP with pretrained weights and configured as trainable, (iii) for signal detection in real RF traces, when compared to training purely with synthetic RF data, a better mAP can be achieved by training with a mixture of synthetic and real RF traces or by finetuning the synthetically trained weights with an additional round of training on a small amount of real RF traces, and (iv) the mAP performance decreases as the signal to noise ratio (SNR) is lowered.

title: Revisiting RCNN: On Awakening the Classification Power of Faster RCNN
abstract: Recent region based object detectors are usually built with separate classification and localization branches on top of shared feature extraction networks. In this paper, we analyze failure cases of state of the art detectors and observe that most hard false positives result from classification instead of localization. We conjecture that: (1) Shared feature representation is not optimal due to the mismatched goals of feature learning for classification and localization; (2) multi task learning helps, yet optimization of the multi task loss may result in sub optimal for individual tasks; (3) large receptive field for different scales leads to redundant context information for small objects.We demonstrate the potential of detector classification power by a simple, effective, and widely applicable Decoupled Classification Refinement (DCR) network. DCR samples hard false positives from the base classifier in Faster RCNN and trains a RCNN styled strong classifier. Experiments show new state of the art results on PASCAL VOC and COCO without any bells and whistles.

title: Assets management on electrical grid using Faster RCNN
abstract: Electrical utility companies around the world are keeping track of all equipment on their distribution grid, because it will help them improve the management and the quality of the services they offer to their customers. Asset management of the electric grid is usually conducted manually, which is expensive, time consuming and the results obtained are often not accurate. In this article an automated asset management system for electricity, transport infrastructures is proposed, it is based on images taken by drones and analysed by Faster Region proposal Convolutional Neural Networks (Faster RCNN) to generate the inventory. The designs of CNN are inspired from the human brain structures, they have been applied to many fields such as object recognition and crowed counting with promising results that are proven to be better than human observer. In order to evaluate the proposed asset management approach, a sample of images was randomly selected from a given dataset, the inventory results generated by the CNN based model are accurate, faster and cheaper than the previous approach based on human observers and helicopters.

title: Detection and Classification of Dental Pathologies using Faster RCNN in Orthopantomogram Radiography Image
abstract: An attempt has been made to device a robust method to detect and classify different oral/dental pathologies using Orthopantomogram (OPD) images based on Faster Regional Convolutional Neural Network (Faster RCNN) The system will provide a novel method for detection and classification of types of teeth (viz. incisors, molar, premolar, canine teeth) and also some underlying oral anomalies such as fixed partial denture and impacted teeth. Initially, various image pre processing techniques are performed. For each input signal containing teeth of a person, detection is done using the concept of Anchors and Intersection over Union. The main advantage of our algorithm is the detection using bounding boxes which replace the manual separation of each individual tooth from the set of teeth of the signal. The proposed algorithm is implemented for training and testing and also compared with the ground truth and our model gives an accuracy of more than 90% for detection and 99% for classification.

title: An Implementation of Faster RCNN with Study for Region Sampling
abstract: We adapted the join training scheme of Faster RCNN framework from Caffe to TensorFlow as a baseline implementation for object detection. Our code is made publicly available. This report documents the simplifications made to the original pipeline, with justifications from ablation analysis on both PASCAL VOC 2007 and COCO 2014. We further investigated the role of non maximal suppression (NMS) in selecting regions of interest (RoIs) for region classification, and found that a biased sampling toward small regions helps performance and can achieve on par mAP to NMS based sampling when converged sufficiently.