Target detection method based on dense connection structure

Abstract

The invention provides a target detection method based on a dense connection structure, and the method comprises the steps: defining a to-be-detected target type, labeling a target object in collectedimage data, obtaining the actual frame of the target object in the image data, and marking the target type of the target object, and obtaining a data set; constructing a target detection network model composed of a basic network module, a feature fusion module, a dense connection module and a feature aggregation module, and determining a loss function; training the constructed target detection network model by using the data set until the loss function converges, finishing the training process, and storing the corresponding weight parameter at the moment to obtain a trained target detection network model; and inputting the image of the to-be-detected target category into the trained target detection model to realize target detection. According to the method, a dense connection mode and afeature fusion and aggregation mode are combined, so that the feature extraction capability is improved, the gradient descent problem is relieved, and the detection efficiency and accuracy are effectively improved.

Description

technical field [0001] The invention relates to the technical fields of deep convolutional neural network and computer vision based on deep learning, in particular to a target detection method based on a dense connection structure. Background technique [0002] With the rapid development of information technology, massive amounts of data are generated in every aspect of people's lives. Image is one of many data types. It is a basic research goal to understand the information content conveyed by image data and a basic task of computer vision. [0003] In recent years, with the advancement of high-performance computing technology and the emergence of high-efficiency computing equipment, the development of deep learning and artificial intelligence technology, object detection technology has been widely used in many aspects such as robot vision, consumer electronics, intelligent video surveillance, and content-based image retrieval. application. However, when using traditional...

AI Technical Summary

Problems solved by technology

Since it is a basic network proposed for classification tasks, when it is used for target detection, it has the disadvantages of low feature extraction ability and inability to make full use of multi-scale regional features, which will have an impact on the classification and positioning of the subsequent detection network, thereby affecting detection. Accuracy rate, resulting in low accuracy rate

Images