Target detection method for lightweight side-scan sonar shipwreck image

By building an improved YOLOv8 network model, combining weighted feature fusion and multi-scale convolution module, the balance problem of high-precision and rapid detection in side-sweep sonar technology is solved, and is suitable for underwater object detection.

CN120411752APending Publication Date: 2025-08-01NORTHEAST DIANLI UNIVERSITY
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Patent Information

Application Number
CN202510461100.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-04-14
Publication Date
2025-08-01

AI Technical Summary

Technical Problem

The existing side-sweep sonar technology cannot have both high precision and fast detection at the same time. The traditional method has low detection efficiency and is easily affected by subjective factors, while the deep learning method has high computational volume and is difficult to meet real-time needs.

Method used

Build an improved network model based on YOLOv8, including backbone improvement network, feature fusion improvement network and lightweight shared detection head, and adopts a weighted feature fusion mechanism, multi-scale convolution module and efficient upsampling module to reduce the amount of parameters and improve the feature fusion capability.

Benefits of technology

It realizes high-precision and rapid detection under limited computing resources, significantly reducing model complexity and improving detection accuracy and speed, and is suitable for real-time object detection of underwater autonomous vehicles.

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Abstract

The invention provides a target detection method for lightweight side-scan sonar shipwreck images, and the method comprises the steps: obtaining a target data set of side-scan sonar shipwreck detection, and the target data set comprises a plurality of shipwreck images; constructing an improved network model of the YOLO; the improved network model of the YOLO comprises a trunk improved network, a feature fusion improved network and a lightweight shared detection head; the main improved network comprises a plurality of layers of first improved cross-stage partial aggregation modules; the feature fusion improved network comprises a plurality of layers of second improved cross-stage partial aggregation modules and a plurality of layers of up-sampling improved modules; the lightweight sharing detection head comprises a sharing convolution module; training the improved network model by using the target data set to obtain a target improved network model; and acquiring a real-time data set of side-scan sonar shipwreck detection, and performing target detection on the real-time data set by using the target network model. According to the technical scheme, the problem that the existing side-scan sonar technology cannot have high precision and rapid detection at the same time is solved.
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