An adaptive matching method for deep and shallow features
By coordinating the contributions of deep and shallow features through adaptive weighting operations, the problem of limited performance in small target detection in deep neural networks is solved, achieving more efficient detection results.
CN116681981BActive Publication Date: 2026-06-26JIANGSU UNIV OF SCI & TECH
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- JIANGSU UNIV OF SCI & TECH
- Filing Date
- 2023-03-17
- Publication Date
- 2026-06-26
AI Technical Summary
Technical Problem
In small target detection, the rigid splicing of deep and shallow features in existing deep neural networks cannot fully coordinate their contributions, thus limiting the detection performance.
Method used
An adaptive weighted mask generation submodule is adopted to coordinate the contribution of deep and shallow features through adaptive weighting operations, thereby achieving feature matching.
Benefits of technology
It significantly improves the accuracy of small target detection, reduces false detections and missed detections, and enhances detection performance.
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Figure CN116681981B_ABST
Abstract
The application discloses a kind of self-adapting matching methods for deep, shallow feature, as follows: data preprocessing, data set is converted into the format that network model can operate;Model selection, select appropriate single-stage target detection detection network;Model editing, by adding deep, shallow feature matching module in model FPN structure, use difference mask to weight deep, shallow feature, coordinate the contribution of different level features to terminal prediction model;Model training, use binary cross-entropy loss and IOU loss to optimize the model;Model evaluation, use the trained model to predict the class and position of test sample.The application has higher detection target precision, lower miss detection rate, and can effectively solve the problem of insufficient detection precision of single-stage target detection model for small targets.
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