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Ship Target Detection Algorithm Based on Improved Faster R-CNN

Electronics, 2019
Ship target detection has urgent needs and broad application prospects in military and marine transportation. In order to improve the accuracy and efficiency of the ship target detection, an improved Faster R-CNN (Faster Region-based Convolutional Neural
Liang Qi, Liang Dong, Jing Huang, Dong Wang, Xuan Jia, Ganmin Xue, Liankai Chen, Wei Wang, Bangyu Li, Chengwei Ge   +9 more
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Insulator Defect Recognition Based on Faster R-CNN

2020 International Conference on Computer, Information and Telecommunication Systems (CITS), 2020
Insulators are important parts to ensure the normal operation of transmission lines. The traditional method is to judge the defect of the insulators through human eyes, which is not only low in efficiency, but also strong and dangerous in work. In this paper, the most representative Resnet-50 and Faster R-CNN frameworks in target classification ...
Yifan Wang, Zhongxu Li, Xuecheng Yang, Ning Luo, Yu Zhao, Gang Zhou   +5 more
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An Improved Faster R-CNN for Object Detection

2018 11th International Symposium on Computational Intelligence and Design (ISCID), 2018
Among various target detection algorithms, Faster R-CNN is an algorithm with excellent performance both in detection accuracy and in detection speed at present. However, it still has some shortcomings such as too many negative samples. To address the problem of Faster R-CNN, two strategies, hard negative sample mining and alternating training, are ...
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CAPTCHA Recognition Based on Faster R-CNN

2017
In this paper, Faster R-CNN was employed to recognize the CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart). Unlike traditional method, the proposed method is based on deep learning object detection framework. By inputting the database into the network and training the Faster R-CNN, the feature map can be obtained ...
Feng-Lin Du, Jiaxing Li, Zhi Yang, Peng Chen 0001, Bing Wang 0004, Jun Zhang 0011   +5 more
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Contextual Priming and Feedback for Faster R-CNN

2016
The field of object detection has seen dramatic performance improvements in the last few years. Most of these gains are attributed to bottom-up, feedforward ConvNet frameworks. However, in case of humans, top-down information, context and feedback play an important role in doing object detection.
Abhinav Shrivastava, Abhinav Gupta 0001
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Automated Masonry crack detection with Faster R-CNN

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), 2021
Inspection of masonry buildings, typically railway bridges, for crack detection is currently performed by humans under tedious and sometimes dangerous working conditions. Over the past years, computer vision based techniques have been developed to automate structure visual inspections.
Borja Marin, Keith E. Brown, Mustafa Suphi Erden   +2 more
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MI-FGSM on Faster R-CNN Object Detector

2020 The 4th International Conference on Video and Image Processing, 2020
The adversarial examples show the vulnerability of deep neural networks, which makes adversarial attacks widely concerned. However, most of the attack methods are based on image classification model. In this paper, we use Momentum Iterative Fast Gradient Sign Method (MI-FGSM), which stabilize optimization and escape from poor local maxima, to generate ...
Zhenghao Liu, Wenyu Peng, Jun Zhou 0015, Zifeng Wu, Jintao Zhang, Yunchun Zhang   +5 more
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A closer look at Faster R-CNN for vehicle detection

2016 IEEE Intelligent Vehicles Symposium (IV), 2016
Faster R-CNN achieves state-of-the-art performance on generic object detection. However, a simple application of this method to a large vehicle dataset performs unimpressively. In this paper, we take a closer look at this approach as it applies to vehicle detection.
Quanfu Fan, Lisa M. Brown, John Smith 0001   +2 more
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Traffic Signs Detection Based on Faster R-CNN

2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW), 2017
In this paper, we use a advanced method called Faster R-CNN to detect traffic signs. This new method represents the highest level in object recognition, which don't need to extract image feature manually anymore and can segment image to get candidate region proposals automatically. Our experiment is based on a traffic sign detection competition in 2016
Zhongrong Zuo, Kai Yu, Qiao Zhou, Xu Wang, Ting Li   +4 more
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Fingerprint pose estimation based on faster R-CNN

2017 IEEE International Joint Conference on Biometrics (IJCB), 2017
Fingerprint pose estimation is one of the bottlenecks of indexing in large scale database. The existing methods of pose estimation are based on manually appointed features (e.g. special points, ridges, orientation filed). In this paper, we propose a method based on deep learning to achieve accurate pose estimation. Faster R-CNN is adopted to detect the
Jiahong Ouyang, Jianjiang Feng, Jiwen Lu, Zhenhua Guo 0001, Jie Zhou 0001   +4 more
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