Multi-scale target detection method based on deep convolutional neural network

Abstract

The invention discloses a multi-scale target detection method based on a deep convolutional neural network. The method comprises the steps of (1) data acquisition; (2) data processing; (3) model construction; (4) loss function definition; (5) model training; and (6) model verification. The method combines the ability of extracting image high-level semantic information of the deep convolutional neural network, the ability of generating candidate regions of region generation networks, the repair and mapping abilities of a content-aware region-of-interest pooling layer and the precise classification ability of multi-task classification networks, and therefore multi-scale target detection is completed more accurately and efficiently.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to a multi-scale target detection method based on a deep convolutional neural network. Background technique [0002] Object detection and recognition is one of the important topics in the field of computer vision computing. With the development of human science and technology, the important technology of target detection is constantly being fully utilized. People apply it to various scenarios to achieve various expected goals, such as battlefield alert, safety detection, traffic control, video surveillance, etc. aspect. [0003] In recent years, with the rapid development of deep learning, deep convolutional neural networks have made further breakthroughs in target detection and recognition technology. Using the deep convolutional neural network, the high-level semantic feature information of the picture can be extracted, and then these high-level semantic inform...

AI Technical Summary

Problems solved by technology

The deeper the neural network is, the more representative the feature information it expresses, but the problem is that it is very rough for small-scale objects, and even some features of small-scale objects will be lost. The size scale is very sensitive, and there are great differences in the feature information extracted by the neural network for objects of different sizes and scales, resulting in low accuracy of small-scale object detection, which greatly reduces the robustness and effectiveness of target detection.

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