Detect Faces Efficiently: A Survey and Evaluations

Feng, Yuantao; Yu, Shiqi; Peng, Hanyang; Li, Yan-Ran; Zhang, Jianguo

doi:10.1109/tbiom.2021.3120412

Cited by 33 publications

(14 citation statements)

References 70 publications

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“…Then, for each watershed, we feed the real-time rainfall predictions into the model, and this is called model inference. As is used in many studies in other domains such as face recognition and autopilot, the deep learning models with millions of parameters only take a millisecond to run for each inference (Feng et al, 2021b). This suggests the model inference does not require high performance computing or HPC systems.…”

Section: Real-time Forecast Frameworkmentioning

confidence: 99%

Real-Time Streamflow Forecasting Framework, Implementation and Post-Analysis Using Deep Learning

Xiang¹,

Demir²

2022

Preprint

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Rainfall-runoff modeling and streamflow prediction using deep learning algorithms have been studied significantly in the last few years. The majority of these studies focus on the simulation and testing of historical datasets. Deployment and operation of a real-time streamflow forecast model using deep learning will face additional data and computational challenges such as inaccurate rainfall forecast data and real-time data assimilation with limited studies guiding on these difficulties. We proposed a real-time streamflow forecast framework that includes pre-event model training using deep learning, real-time data acquisition, and post-event analysis. We implemented the framework for 124 USGS gauged watersheds across Iowa to forecast 120-hour streamflow rates since April 2021. This is the first time deep learning models have been used to predict streamflow in real-time operational settings at a large scale, and we anticipate seeing more real-time implementations of deep learning models in the future.

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Section: Real-time Forecast Frameworkmentioning

confidence: 99%

Real-Time Streamflow Forecasting Framework, Implementation and Post-Analysis Using Deep Learning

Xiang¹,

Demir²

2022

Preprint

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“…According to the guideline proposed by [21], we divide the existing face detection techniques into two groups: Accuracy-Focusing Face Detection and Speed-Focusing Face Detection. The criterion of this categorization is mainly based on the floating-point operations (FLOPs) of the model.…”

Section: Related Workmentioning

confidence: 99%

“…Our FAFD model was compared with some speed-focusing methods [30][31][32]34] reported in [21]. The results include AP comparisons for Easy, Medium, and Hard subdatasets.…”

Section: Comparative Studymentioning

confidence: 99%

“…Also, the speed performance was compared in terms of latency for VGA-resolution images. For a fair comparison, we export our FAFD-small and FAFD-Nano models from Pytorch to ONNX and inference using ONNXTUNTIME as [21].…”

Section: Comparative Studymentioning

confidence: 99%

“…Although the number of parameters of FAFD-Small is large, its computing speed in terms of the GFLOPs is not that bad. Recall that, in [21], the criterion for differentiating the Speed-Focusing Face Detector from the Accuracy-Focusing Face Detector is the GFLOPs. Here, both FAFD-Nano and FAFD-Small still satisfy the GFLOPs criterion for the Speed-Focusing Face Detector, having a computational magnitude of tens of GFLOPs.…”

Section: Comparative Studymentioning

confidence: 99%

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FAFD: Fast and Accurate Face Detector

Kim

Won

2022

Electronics

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Deep Neural Networks (DNN) have contributed a significant performance improvement in face detection. However, since most models focus only on the improvement of detection accuracy with computationally expensive structures, it is still far from real-time applications with a fast face detector. The goal of this paper is to improve face detection performance from the speed-focusing point of view. To this end, we propose a novel Fast and Accurate Face Detector (FAFD) to achieve high performance on both speed and accuracy performance. Specifically, based on the YOLOv5 model, we add one prediction head to increase the detection performance, especially for small faces. In addition, to increase the detection performance of multi-scale faces, we propose to add a novel Multi-Scale Image Fusion (MSIF) layer to the backbone network. We also propose an improved Copy-Paste to augment the training images with face objects in various scales. Experimental results on the WiderFace dataset show that the proposed FAFD achieves the best performance among the existing methods in a Speed-Focusing group. On three sub-datasets of WiderFace (i.e., Easy, Medium, and Hard sub-datasets), our FAFD yields average precisions (AP) of 95.0%, 93.5%, and 87.0%, respectively. Also, the speed performance of the FAFD is fast enough to be included in the group of speed-focusing methods.

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