yolo object detection

equalization to it, and do some preprocessing work. Next, we will detect the human face and call the detectmultiscale function. This function detects objects at different scales of the input image. The parameter image is the input grayscale image, and the objects is the rectangular frame vector group of the object to be detected, scalefactor is the scale parameter in each image scale. The default value is 1.1. The minneighbors parameter is the number

Target Detection Based on Hof transformation and generalized Hof Transformation The previous section discussed the Target Detection Based on threshold processing. Today we will discuss the Target Detection Based on Hoff voting. Hoff voting intends to be divided into two sections, in the first section, we will briefly describe the HUF transformation and th

application of non-maximal value suppression in object detection The application of NMS algorithm in object detection is introduced according to the source code of py_cpu_nms.py faster-rcnn. After the RPN layer in the FASTER-RCNN, some boundingbox and boundingbox corresponding to a certain class of scores (confidence

1. Migration Learning Migration learning is also known as supervised pre-training (supervised pre-training), which we often call migration learning. For example, you already have a bunch of picture data labeled for the age of the face, trained a CNN to recognize the age of the human face. Then when you meet a new project task is: Face sex recognition, then you can use the trained age to identify the CNN model, remove the last layer, and then the other network layer parameters are copied directly

Original link: 46914837#commentseditThe goal of this paper is to use SIFT and RANSAC algorithm, to complete the correct matching of feature points, and to find the transformation matrix, through the transformation matrix to identify the boundary of the object (the article has some source code, the whole project I also uploaded, please click here).Sift algorithm is currently recognized as the most effective feature point

function of p, and the P is extended to the real field, and the most valued point isThe function rises first and then drops, the limit is 0. Suppose that when P=1 is the desired distance of the mean, there will be a lot of p, which can make the distance increase. Suppose that, if, can be rolled out, This indicates that one of its selected features represents more than half of the patches in the image (this sentence I understand is, because that is the probability of selecting/generating feature

Opencv 2.4 implements the DPM program in C ++. The main difference between it and the previous C version is that it can detect multiple targets at the same time. During use, you can put the trained model in a folder, and put the image to be detected in another folder for detection. Unfortunately, the accelerated content is not considered. Latent SVM regression ¶ Discriminatively trained Part Based Models for obje