An End-to-End Object Detection Method Based on Convolutional Neural Networks

Abstract

The invention discloses an end-to-end target detection method based on a convolutional neural network, comprising: (1) based on a classic basic network structure, removing the last fully connected layer of the classic basic network, and adding an additional layer to establish a convolutional neural network Model; (2) Randomly select an original image from the original training data set for data amplification to obtain an enlarged image, and obtain the position and frame of the target image block randomly selected in the original image in the enlarged image; (3) Using the position and boundary of the target image block obtained in step (2) in the amplified image, the convolutional neural network model in the regression step (2) obtains the model parameters, thereby obtaining the trained convolutional neural network model; (4) Using the trained convolutional neural network model, detect the bounding box and category of the object in the image to be detected. This method uses the direct regression of the target center point coordinates, width, height and category, compared with similar methods, it has a great advantage in speed.

Description

technical field [0001] The invention belongs to the field of computer vision, and more specifically, relates to an end-to-end object detection method based on a convolutional neural network. Background technique [0002] Target detection is a basic task in computer vision. It can be used in many common projects in reality, such as pedestrian detection, vehicle detection, target tracking and preprocessing in image retrieval. Doing a good job in target detection is very helpful for some higher-level tasks. Most of the existing target detection algorithms are algorithms with high complexity, and few can achieve real-time. Therefore, developing a set of high-precision and fast detection algorithms has always been a difficult problem in computer vision. Contents of the invention [0003] In view of the above defects or improvement needs of the prior art, the present invention provides an end-to-end object detection method based on a convolutional neural network, which has high...

AI Technical Summary

Problems solved by technology

Most of the existing target detection algorithms are algorithms with high complexity, which rarely achieve real-time performance. Therefore, developing a set of high-precision and fast detection algorithms has always been a difficult problem in computer vision.

Images