Target detection improved algorithm based on feature pyramid network and attention mechanism

Abstract

The invention discloses a target detection improved algorithm based on a feature pyramid network and an attention mechanism, and the method comprises the steps: carrying out the fusion of six multi-scale feature maps extracted by a basic network in an original SSD algorithm through combining the principle of the feature pyramid network, and enabling a new feature map formed after fusion to containrich context information, the detection capability is improved; and adding an attention model to the fused feature map, so that the feature information of the small target is effectively extracted. The condition of missing detection is improved, the robustness of the algorithm is improved, and meanwhile, the real-time requirement is still met in the aspect of detection speed.

Description

technical field [0001] The invention belongs to the field of digital image processing and relates to target detection, in particular to an improved target detection algorithm based on a feature pyramid network and an attention mechanism. Background technique [0002] The task of object detection is to find out the objects of interest in the image, determine their category and location, which is one of the core issues in the field of computer vision, in infrared detection technology, intelligent video surveillance, remote sensing image object detection, medical diagnosis and intelligent building It is widely used in fire and smoke detection. Target detection algorithms can be divided into traditional target detection algorithms and deep learning-based target detection algorithms; representative algorithms of traditional target detection algorithms include SIFT algorithm and V-J detection algorithm, etc., but this method has high time complexity and is not very robust sex. T...

AI Technical Summary

Problems solved by technology

Although there are many excellent target detection algorithms at this stage, there are still many shortcomings in the detection performance, which leads to problems such as missed detection and false detection.

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