Fbi Faces 4.0.epub [EXCLUSIVE]
LINK ===== https://urllio.com/2tgxZg
In addition to varying and often restrictive definitions, research on mass homicides faces several other limitations. These include focusing only on certain victim or offender types (Fox & Levin, 2015); excluding non-random incidents, such as those related to crime or relationship problems (Taylor, 2018); reliance on convenience samples such as media accounts; inclusion of only firearm homicides or only those that occur in public; and lack of systematic comparison with other types of homicides that may identify unique characteristics of these incidents. These limitations may leave important points of prevention unstudied.
Abstract: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.Keywords: face detection; convolution neural network; YOLOv5; image augmentation 153554b96e
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