scholarly journals Towards Robust Object Detection in Floor Plan Images: A Data Augmentation Approach

2021 ◽  
Vol 11 (23) ◽  
pp. 11174
Author(s):  
Shashank Mishra ◽  
Khurram Azeem Hashmi ◽  
Alain Pagani ◽  
Marcus Liwicki ◽  
Didier Stricker ◽  
...  
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Data Augmentation ◽  
State Of The Art ◽  
Floor Plan ◽  
Previous State ◽  
Floor Plans ◽  
Better Than

Object detection is one of the most critical tasks in the field of Computer vision. This task comprises identifying and localizing an object in the image. Architectural floor plans represent the layout of buildings and apartments. The floor plans consist of walls, windows, stairs, and other furniture objects. While recognizing floor plan objects is straightforward for humans, automatically processing floor plans and recognizing objects is challenging. In this work, we investigate the performance of the recently introduced Cascade Mask R-CNN network to solve object detection in floor plan images. Furthermore, we experimentally establish that deformable convolution works better than conventional convolutions in the proposed framework. Prior datasets for object detection in floor plan images are either publicly unavailable or contain few samples. We introduce SFPI, a novel synthetic floor plan dataset consisting of 10,000 images to address this issue. Our proposed method conveniently exceeds the previous state-of-the-art results on the SESYD dataset with an mAP of 98.1%. Moreover, it sets impressive baseline results on our novel SFPI dataset with an mAP of 99.8%. We believe that introducing the modern dataset enables the researcher to enhance the research in this domain.

scholarly journals Towards Robust Object detection in Floor Plan Images: A Data Augmentation Approach

2021 ◽  
Author(s):  
Shashank Mishra ◽  
Khurram Azeem Hashmi ◽  
Alain Pagani ◽  
Marcus Liwicki ◽  
Didier Stricker ◽  
...  
Keyword(s):  
Object Detection ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
The Novel ◽  
Floor Plan ◽  
Previous State ◽  
Floor Plans ◽  
Public Datasets ◽  
Better Than

Object detection is one of the most critical tasks in the field of Computer vision. This task comprises identifying and localizing an object in the image. Architectural floor plans represent the layout of buildings and apartments. The floor plans consist of walls, windows, stairs, and other furniture objects. While recognizing floor plan objects is straightforward for humans, automatically processing floor plans and recognizing objects is a challenging problem. In this work, we investigate the performance of the recently introduced Cascade Mask R-CNN network to solve object detection in floor plan images. Furthermore, we experimentally establish that deformable convolution works better than conventional convolutions in the proposed framework. Identifying objects in floor plan images is also challenging due to the variety of floor plans and different objects. We faced a problem in training our network because of the lack of publicly available datasets. Currently, available public datasets do not have enough images to train deep neural networks efficiently. We introduce SFPI, a novel synthetic floor plan dataset consisting of 10000 images to address this issue. Our proposed method conveniently surpasses the previous state-of-the-art results on the SESYD dataset and sets impressive baseline results on the proposed SFPI dataset. The dataset can be downloaded from SFPI Dataset Link. We believe that the novel dataset enables the researcher to enhance the research in this domain further.

scholarly journals A Hard Example Mining Approach for Concealed Multi-Object Detection of Active Terahertz Image

2021 ◽  
Vol 11 (23) ◽  
pp. 11241
Author(s):  
Ling Li ◽  
Fei Xue ◽  
Dong Liang ◽  
Xiaofei Chen
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
State Of The Art ◽  
Terahertz Imaging ◽  
Public Security ◽  
Counter Terrorism ◽  
Detection Algorithms ◽  
Public Dataset ◽  
The One ◽  
Objects Detection

Concealed objects detection in terahertz imaging is an urgent need for public security and counter-terrorism. So far, there is no public terahertz imaging dataset for the evaluation of objects detection algorithms. This paper provides a public dataset for evaluating multi-object detection algorithms in active terahertz imaging. Due to high sample similarity and poor imaging quality, object detection on this dataset is much more difficult than on those commonly used public object detection datasets in the computer vision field. Since the traditional hard example mining approach is designed based on the two-stage detector and cannot be directly applied to the one-stage detector, this paper designs an image-based Hard Example Mining (HEM) scheme based on RetinaNet. Several state-of-the-art detectors, including YOLOv3, YOLOv4, FRCN-OHEM, and RetinaNet, are evaluated on this dataset. Experimental results show that the RetinaNet achieves the best mAP and HEM further enhances the performance of the model. The parameters affecting the detection metrics of individual images are summarized and analyzed in the experiments.

scholarly journals EFFICIENT SEMANTIC SEGMENTATION OF MAN-MADE SCENES USING FULLY-CONNECTED CONDITIONAL RANDOM FIELD

2016 ◽  
Vol XLI-B3 ◽  
pp. 633-640
Author(s):  
Weihao Li ◽  
Michael Ying Yang
Keyword(s):  
Random Field ◽  
State Of The Art ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Mean Field ◽  
Semantic Segmentation ◽  
Gaussian Kernels ◽  
Previous State ◽  
Fully Connected ◽  
Better Than

In this paper we explore semantic segmentation of man-made scenes using fully connected conditional random field (CRF). Images of man-made scenes display strong contextual dependencies in the spatial structures. Fully connected CRFs can model long-range connections within the image of man-made scenes and make use of contextual information of scene structures. The pairwise edge potentials of fully connected CRF models are defined by a linear combination of Gaussian kernels. Using filter-based mean field algorithm, the inference is very efficient. Our experimental results demonstrate that fully connected CRF performs better than previous state-of-the-art approaches on both eTRIMS dataset and LabelMeFacade dataset.

scholarly journals PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis

2021 ◽  
Vol 9 ◽  
Author(s):  
Shui-Hua Wang ◽  
Ziquan Zhu ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Infectious Disease ◽  
Convolutional Neural Network ◽  
Loss Function ◽  
Data Augmentation ◽  
State Of The Art ◽  
Diagnosis System ◽  
Backbone Network ◽  
The Mean ◽  
Better Than

Objective: COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system.Methods: First, the n-conv module (nCM) is introduced. Then we built a 12-layer convolutional neural network (12l-CNN) as the backbone network. Afterwards, PatchShuffle was introduced to integrate with 12l-CNN as a regularization term of the loss function. Our model was named PSCNN. Moreover, multiple-way data augmentation and Grad-CAM are employed to avoid overfitting and locating lung lesions.Results: The mean and standard variation values of the seven measures of our model were 95.28 ± 1.03 (sensitivity), 95.78 ± 0.87 (specificity), 95.76 ± 0.86 (precision), 95.53 ± 0.83 (accuracy), 95.52 ± 0.83 (F1 score), 91.7 ± 1.65 (MCC), and 95.52 ± 0.83 (FMI).Conclusion: Our PSCNN is better than 10 state-of-the-art models. Further, we validate the optimal hyperparameters in our model and demonstrate the effectiveness of PatchShuffle.

scholarly journals PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Shui-Hua Wang ◽  
Yin Zhang ◽  
Xiaochun Cheng ◽  
Xin Zhang ◽  
Yu-Dong Zhang
Keyword(s):  
Neural Network ◽  
Pulmonary Tuberculosis ◽  
Healthy Subjects ◽  
Data Augmentation ◽  
State Of The Art ◽  
Accurate Diagnosis ◽  
Test Set ◽  
The World ◽  
Stochastic Pooling ◽  
Better Than

Aim. COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. Methods. In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. Results. The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. Conclusion. This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.

scholarly journals Detection and Classification of Interacting Persons

2010 ◽  
pp. 213-227
Author(s):  
Scott Blunsden ◽  
Robert Fisher
Keyword(s):  
State Of The Art ◽  
New Method ◽  
Classification Method ◽  
Previous State ◽  
New Feature ◽  
Better Than

This chapter presents a way to classify interactions between people. Examples of the interactions we investigate are: people meeting one another, walking together, and fighting. A new feature set is proposed along with a corresponding classification method. Results are presented which show the new method performing significantly better than the previous state of the art method as proposed by Oliver et al. (2000).

scholarly journals Automatically Paraphrasing via Sentence Reconstruction and Round-trip Translation

2021 ◽  
Author(s):  
Zilu Guo ◽  
Zhongqiang Huang ◽  
Kenny Q. Zhu ◽  
Guandan Chen ◽  
Kaibo Zhang ◽  
...  
Keyword(s):  
Machine Translation ◽  
Question Answering ◽  
Domain Adaptation ◽  
State Of The Art ◽  
Training Data ◽  
Round Trip ◽  
Previous State ◽  
Supervised Methods ◽  
Paraphrase Generation ◽  
Better Than

Paraphrase generation plays key roles in NLP tasks such as question answering, machine translation, and information retrieval. In this paper, we propose a novel framework for paraphrase generation. It simultaneously decodes the output sentence using a pretrained wordset-to-sequence model and a round-trip translation model. We evaluate this framework on Quora, WikiAnswers, MSCOCO and Twitter, and show its advantage over previous state-of-the-art unsupervised methods and distantly-supervised methods by significant margins on all datasets. For Quora and WikiAnswers, our framework even performs better than some strongly supervised methods with domain adaptation. Further, we show that the generated paraphrases can be used to augment the training data for machine translation to achieve substantial improvements.

scholarly journals Spherical Criteria for Fast and Accurate 360° Object Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 12959-12966
Author(s):  
Pengyu Zhao ◽  
Ansheng You ◽  
Yuanxing Zhang ◽  
Jiaying Liu ◽  
Kaigui Bian ◽  
...  
Keyword(s):  
Object Detection ◽  
Real World ◽  
State Of The Art ◽  
Perspective Projection ◽  
Two Stage ◽  
The Past ◽  
Previous State ◽  
The Common ◽  
Synthetic Datasets ◽  
Bounding Boxes

With the advance of omnidirectional panoramic technology, 360◦ imagery has become increasingly popular in the past few years. To better understand the 360◦ content, many works resort to the 360◦ object detection and various criteria have been proposed to bound the objects and compute the intersection-over-union (IoU) between bounding boxes based on the common equirectangular projection (ERP) or perspective projection (PSP). However, the existing 360◦ criteria are either inaccurate or inefficient for real-world scenarios. In this paper, we introduce a novel spherical criteria for fast and accurate 360◦ object detection, including both spherical bounding boxes and spherical IoU (SphIoU). Based on the spherical criteria, we propose a novel two-stage 360◦ detector, i.e., Reprojection R-CNN, by combining the advantages of both ERP and PSP, yielding efficient and accurate 360◦ object detection. To validate the design of spherical criteria and Reprojection R-CNN, we construct two unbiased synthetic datasets for training and evaluation. Experimental results reveal that compared with the existing criteria, the two-stage detector with spherical criteria achieves the best mAP results under the same inference speed, demonstrating that the spherical criteria can be more suitable for 360◦ object detection. Moreover, Reprojection R-CNN outperforms the previous state-of-the-art methods by over 30% on mAP with competitive speed, which confirms the efficiency and accuracy of the design.

scholarly journals Multiple Receptive Field Network (MRF-Net) for Autonomous Underwater Vehicle Fishing Net Detection Using Forward-Looking Sonar Images

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1933
Author(s):  
Rixia Qin ◽  
Xiaohong Zhao ◽  
Wenbo Zhu ◽  
Qianqian Yang ◽  
Bo He ◽  
...  
Keyword(s):  
Receptive Field ◽  
Embedded System ◽  
Object Detection ◽  
Autonomous Underwater Vehicle ◽  
State Of The Art ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Field Network ◽  
Better Than ◽  
Forward Looking

Underwater fishing nets represent a danger faced by autonomous underwater vehicles (AUVs). To avoid irreparable damage to the AUV caused by fishing nets, the AUV needs to be able to identify and locate them autonomously and avoid them in advance. Whether the AUV can avoid fishing nets successfully depends on the accuracy and efficiency of detection. In this paper, we propose an object detection multiple receptive field network (MRF-Net), which is used to recognize and locate fishing nets using forward-looking sonar (FLS) images. The proposed architecture is a center-point-based detector, which uses a novel encoder-decoder structure to extract features and predict the center points and bounding box size. In addition, to reduce the interference of reverberation and speckle noises in the FLS image, we used a series of preprocessing operations to reduce the noises. We trained and tested the network with data collected in the sea using a Gemini 720i multi-beam forward-looking sonar and compared it with state-of-the-art networks for object detection. In order to further prove that our detector can be applied to the actual detection task, we also carried out the experiment of detecting and avoiding fishing nets in real-time in the sea with the embedded single board computer (SBC) module and the NVIDIA Jetson AGX Xavier embedded system of the AUV platform in our lab. The experimental results show that in terms of computational complexity, inference time, and prediction accuracy, MRF-Net is better than state-of-the-art networks. In addition, our fishing net avoidance experiment results indicate that the detection results of MRF-Net can support the accurate operation of the later obstacle avoidance algorithm.

scholarly journals Convolutional Neural Networks for Classifying Melanoma Images

2020 ◽  
Author(s):  
Abhinav Sagar ◽  
J Dheeba
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Cancer Classification ◽  
Previous State ◽  
Better Than

AbstractIn this work, we address the problem of skin cancer classification using convolutional neural networks. A lot of cancer cases early on are misdiagnosed as something else leading to severe consequences including the death of a patient. Also there are cases in which patients have some other problems and doctors think they might have skin cancer. This leads to unnecessary time and money spent for further diagnosis. In this work, we address both of the above problems using deep neural networks and transfer learning architecture. We have used publicly available ISIC databases for both training and testing our model. Our work achieves an accuracy of 0.935, precision of 0.94, recall of 0.77, F1 score of 0.85 and ROC-AUC of 0.861 which is better than the previous state of the art approaches.

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