Target detection method and system based on improved SSD algorithm

By replacing VGG16 with ResNet-50 and CBAM attention mechanism, and integrating a shallow feature fusion and recursive reverse path, the SSD algorithm achieves improved target detection accuracy and capability for multi-scale targets.

CN119107624AActive Publication Date: 2024-12-10CHILIANG (XIAN) TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202411289311.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-09-14
Publication Date
2024-12-10
Estimated Expiration
2044-09-14

AI Technical Summary

Technical Problem

The deep feature layer of the SSD algorithm lacks fine-grained information, leading to insufficient feature extraction and resulting in false detections and misdetections due to the reliance on shallow feature maps with fewer convolutional layers.

Method used

Replace the original backbone network VGG16 with a new network combining ResNet-50 and an improved CBAM attention mechanism, incorporating a shallow feature fusion module, recursive reverse path with deep feature enhancement and upsampling modules, and utilize the Focal Loss function to enhance target detection accuracy.

Benefits of technology

Improves the detection capability of targets of different scales by constructing a more comprehensive feature pyramid, enhancing the model's ability to accurately locate and classify targets, especially in complex backgrounds and multi-scale scenarios.

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Abstract

The invention provides a target detection method and system based on an improved SSD algorithm, and the method comprises the steps: obtaining a to-be-detected image; acquiring initial input data according to the to-be-detected image; an SSD algorithm is improved based on ResNet50 and a CBAM attention mechanism, and an improved target network is obtained; and determining a target detection result according to the initial input data and the improved target network. According to the target detection method and system based on the improved SSD algorithm, an original backbone network VGG16 of the SSD algorithm is replaced by a novel network in which ResNet-50 and an improved CBAM attention mechanism are combined, a more comprehensive feature pyramid is constructed, the detection capability of targets of different scales is improved, the target detection result is determined according to the initial input data and the improved target network, and the target detection efficiency is improved. And the target detection precision is improved.
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Citation Information

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