Real-time multi-scale target detection method based on lightweight convolutional neural network

Abstract

The invention discloses a real-time multi-scale target detection method based on a lightweight convolutional neural network, and the method comprises the following steps: employing a clustering algorithm to cluster the depth-to-width ratio and the area scale coefficient of a training data set sample target, wherein the clustering center is used for optimizing the setting of an anchor frame; takinga lightweight convolutional neural network as a backbone network of a RetinaNet framework, and reducing the model complexity; enhancing the multi-scale feature map by using a residual error module, performing adaptive cross-layer feature fusion on the partially enhanced multi-scale feature map, and finally, using the generated fusion feature pyramid to replace the feature pyramid, so that the detection precision of the model for small and medium-sized targets is improved. The lightweight multi-scale target detection method disclosed by the invention can reduce the parameter quantity of the model, reduce the operation complexity of the model and improve the detection efficiency of the model under the condition of ensuring a certain accuracy rate.

Description

technical field [0001] The present invention mainly relates to the technical field of target detection based on deep learning, in particular to a real-time multi-scale target detection method based on a lightweight convolutional neural network. Background technique [0002] Recognizing objects of different sizes is a difficult problem in computer vision. Object detection with a wide range of object scales is a very challenging problem. Vehicle and pedestrian detection in autonomous driving scenarios is a typical multi-scale object detection problem. In the videos or images taken based on road conditions, the scale distribution of vehicles and pedestrians is very wide, and the small and medium scale objects account for a large proportion. General-purpose target detection algorithms have low detection accuracy for small and medium-sized vehicles and pedestrians. The Feature Pyramid Network (FPN) provides multi-layer fusion features, which is an effective way to achieve mult...

AI Technical Summary

Problems solved by technology

General-purpose target detection algorithms have low detection accuracy for small and medium-sized vehicles and pedestrians

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