Fault Detection Based on Fully Convolutional Networks (FCN)
نویسندگان
چکیده
منابع مشابه
R-FCN: Object Detection via Region-based Fully Convolutional Networks
We present region-based, fully convolutional networks for accurate and efficient object detection. In contrast to previous region-based detectors such as Fast/Faster R-CNN [7, 19] that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image. To achieve this goal, we propose position-sensitive...
متن کاملSaliency Detection with Recurrent Fully Convolutional Networks
• Employs three kind of low-level contrast features, including color, intensity and orientation, and the center prior knowledge to introduce saliency prior maps. • Train the RFCN with two stage training strategy, pre-training on the segmentation data set and fine-tuning on the saliency data set. The recurrent structure can incorporate the saliency prior maps into the CNNs with an end-to-end tra...
متن کاملOn Study of the Reliable Fully Convolutional Networks with Tree Arranged Outputs (TAO-FCN) for Handwritten String Recognition
The handwritten string recognition is still a challengeable task, though the powerful deep learning tools were introduced. In this paper, based on TAO-FCN, we proposed an end-to-end system for handwritten string recognition. Compared with the conventional methods, there is no preprocess nor manually designed rules employed. With enough labelled data, it is easy to apply the proposed method to d...
متن کاملDetection and Tracking of Liquids with Fully Convolutional Networks
Recent advances in AI and robotics have claimed many incredible results with deep learning, yet no work to date has applied deep learning to the problem of liquid perception and reasoning. In this paper, we apply fully-convolutional deep neural networks to the tasks of detecting and tracking liquids. We evaluate three models: a single-frame network, multi-frame network, and a LSTM recurrent net...
متن کاملRidiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
Shot boundary detection (SBD) is an important component of many video analysis tasks, such as action recognition, video indexing, summarization and editing. Previous work typically used a combination of low-level features like color histograms, in conjunction with simple models such as SVMs. Instead, we propose to learn shot detection end-to-end, from pixels to final shot boundaries. For traini...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Marine Science and Engineering
سال: 2021
ISSN: 2077-1312
DOI: 10.3390/jmse9030259