Complex environment image semantic segmentation method based on fine-grained enhancement network

By using fine-grained enhancement networks to perform image semantic segmentation in complex environments, the problem of insufficient fine-grained recognition capabilities in the prior art is solved, and higher semantic segmentation accuracy and detail recovery capabilities are achieved.

CN119723086AActive Publication Date: 2025-03-28CHONGQING UNIV OF TECH +1
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Patent Information

Application Number
CN202411843663.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-14
Publication Date
2025-03-28
Estimated Expiration
2044-12-14

AI Technical Summary

Technical Problem

The existing technology lacks fine-grained recognition capabilities in complex environments such as mountains and hills, resulting in room for improvement in semantic segmentation accuracy.

Method used

The complex environment image semantic segmentation method based on fine-grained enhancement network is adopted to extract global and local features through the backbone network layer, and upsample and detail enhancement processing are performed in the neck network layer. Combined with the C2f-SSA module and the Dysample-ConvBN upsample module, the feature extraction and detail recovery capabilities of the model are improved.

Benefits of technology

It realizes more accurate semantic segmentation in complex environments, improves fine-grained recognition capabilities and segmentation accuracy, reduces image noise, and saves computing resources.

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Abstract

The invention discloses a complex environment image semantic segmentation method based on a fine-grained enhancement network, and the method comprises the steps: obtaining a to-be-detected environment image, inputting the to-be-detected environment image into a pre-trained fine-grained enhancement network model, and obtaining a semantic segmentation result of the to-be-detected environment image. According to the method, a fine-grained enhancement network model constructed through optimization is utilized, global feature information and local feature information are subjected to deep extraction and multi-scale fusion through a backbone network layer, the feature extraction capability of the model is enhanced, and then the feature extraction capability of the model is enhanced by means of up-sampling and detail enhancement processing of a neck network layer. The detail recovery capability of the model is enhanced, the image noise is effectively reduced, the semantic segmentation result of the environment image is obtained through the detection head, and accurate semantic segmentation for the complex environment image is realized.
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