A Dense Face Detection Method Based on Cascaded Multiscale

Abstract

The invention relates to a method for dense face detection based on cascaded multi-scale, which trains detectors of various scale ranges respectively, each object detector is aimed at a specific scale range, and then cascades them to optimize the existing The network structure, this strategy can be carried in any deep model of face detection, has good scalability, and is more suitable for dense small face detection. It can be applied to specific scenarios such as dense crowd monitoring and classroom population statistics, and has strong application value.

Description

technical field [0001] The invention relates to the fields of deep learning and computer vision, in particular to a method for detecting dense small faces based on cascaded multi-scales. Background technique [0002] Deep learning has shown great vitality in the fields of image classification and object detection. In the last five years, since AlexNet was proposed, the error rate on the ImageNet dataset has dropped from 15% to 2%, surpassing the human level. On the other hand, in the field of object detection, the best performing detector only achieves 60% mAp in the COCO dataset. Why is object detection relatively difficult for image classification? [0003] This is because object detection is different from image classification tasks, and the image size of image classification is often fixed. For convolutional neural networks, a single-scale image is especially suitable for the invariant feature of convolution kernel convolution. Due to the well-trained depth The weight ...

AI Technical Summary

Problems solved by technology

This is a perfect attempt of deep learning in the field of image classification, and it exceeds the human level, but unlike the classification task, object detection not only needs to give the calculated object category, but also gives the position information of the object in the image , which undoubtedly increases the difficulty of object detection, and, in an image, there will be objects of different scales, some objects occupy only a few pixels, which further increases the difficulty of object detection

Since there are various difficulties in object detection, and the scale range of objects with the same label may be very large, this is a huge challenge for the scale invariance of convolutional neural networks. In datasets with large scale range changes, a Detectors must accommodate objects of various scales

In addition to the problem of large scale changes, when the density of objects in the image is too dense, there will be missed detection, so this is also one of the problems to be solved

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