Pedestrian detection method based on deep convolutional neural network

Abstract

The invention discloses a pedestrian detection method based on a deep convolutional neural network, which includes the following steps: (10) sample set collection: divide the sample set image into a training set and a test set; (20) sample image preprocessing: process the sample set Image size transformation, contrast normalization, and whitening processing are performed to obtain low-redundancy sample grayscale images; (30) deep convolutional network model acquisition: use low-redundant training samples and adopt continuous dropout strategy to perform deep convolutional network Training, using the test set samples to test the deep convolutional network to obtain a deep convolutional network model; (40) Pedestrian detection: use the deep convolutional network model to detect pedestrians on the scene images. The pedestrian detection method based on the deep convolutional network of the present invention obtains better generalization ability while maintaining the network training accuracy through the continuous dropout strategy, thereby obtaining a higher accuracy rate in pedestrian detection.

Description

technical field [0001] The invention belongs to the technical field of image recognition, in particular to a pedestrian detection method based on a deep convolutional neural network with fast training convergence speed and strong generalization ability. Background technique [0002] In the field of computer vision, pedestrian detection is a very important research content, and it is widely used in scenarios such as autonomous driving of cars and crowd monitoring in public places. [0003] The traditional pedestrian detection technology is to detect pedestrians by artificially designing features, such as HOG-features, and training classifiers. In the face of scene changes and a sharp increase in the number of detection objects, the cost of artificially designing features is too high to meet real-time requirements and robustness. [0004] Since 2006, deep learning has developed rapidly and has been widely used in areas such as image classification, pattern recognition, and vi...

AI Technical Summary

Problems solved by technology

[0007] To sum up, the problem existing in the existing technology is: when the pedestrian detection is based on the deep convolutional neural network, when the dropout strategy is introduced to enhance the generalization ability of the convolutional network, the trainability of the network may decrease.

Images