End-to-end lightweight underwater target detection method and system

By adopting an end-to-end lightweight underwater target detection method, combined with a lightweight image enhancement module and a low-parameter convolutional structure, the problems of cumbersome models and slow processing in underwater target detection systems are solved, achieving efficient and stable underwater target detection, which is suitable for complex environments and resource-constrained platforms.

CN120913050AActive Publication Date: 2025-11-07齐鲁空天信息研究院
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Patent Information

Application Number
CN202511439489.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-10
Publication Date
2025-11-07
Estimated Expiration
2045-10-10

AI Technical Summary

Technical Problem

Existing underwater target detection systems suffer from excessive model parameters, high computational resource consumption, and slow inference speed, resulting in response delays and increased recognition error rates. They cannot meet the technical requirements of underwater operations for high real-time performance, lightweight deployment, and stable recognition.

Method used

An end-to-end lightweight underwater target detection method is adopted, which combines a lightweight image enhancement module, a low-parameter convolutional structure, and a multi-scale target perception mechanism with edge computing devices to achieve localized inference of the model, thereby reducing resource consumption and improving detection speed and accuracy.

Benefits of technology

A lightweight target detection system suitable for complex underwater environments was constructed, which improved the system's processing speed and detection accuracy, enhanced the model's operating efficiency and deployment flexibility on resource-constrained platforms, and achieved fast, stable, and accurate underwater target detection.

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Abstract

The invention provides an end-to-end lightweight underwater target detection method and system, and belongs to the field of target detection, and the method comprises the steps: carrying out the frame sampling of an underwater video stream at a fixed interval, and obtaining an original image as a to-be-detected image; sending the original image into a differentiable brightness enhancement module and a lightweight texture enhancement module in parallel to generate a color correction image and a texture enhancement image in sequence, and inputting the original image, the color correction image and the texture enhancement image into a space correlation gating attention fusion module to obtain a fusion enhancement image; inputting the fusion enhancement graph into a backbone network formed by connecting a plurality of feature extraction layers in series and then connecting the feature extraction layers in series with a spatial pyramid pooling layer, and outputting a multi-scale feature graph; sending the multi-scale feature map into a path aggregation network to obtain a fused feature map with consistent semantics; and inputting the fusion feature map and the output of the spatial pyramid pooling layer into a multi-branch detection head, completing category, bounding box and confidence prediction, and outputting a final detection result through non-maximum suppression.
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