JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2026, Vol. 61 ›› Issue (1): 15-25.doi: 10.6040/j.issn.1671-9352.8.2024.013

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Lightweight water surface small object detection model with multi-scale attention mechanism and improved feature fusion

ZHONG Shang1, MA Li1,2*, LIU Wenzhe1, LI Yuhao1   

  1. 1. College of Information Engineering, Hebei GEO University, Shijiazhuang 052161, Hebei, China;
    2. Intelligent Sensor Network Engineering Research Center of Hebei Province, Hebei GEO University, Shijiazhuang 052161, Hebei, China
  • Published:2026-01-15

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Abstract: In complex water surface scenarios, addressing the issues of low detection accuracy, high missed detection rates, and limited computational resources for small target detection, this paper proposes a lightweight water surface small object detection model with multi-scale attention mechanism and improved feature fusion. Based on centerness theory, a new backbone network is designed, leveraging the multi-scale attention mechanism to enhance the models feature extraction capabilities. Partial convolution is used to improve the neck network by reducing feature map redundancy, effectively lowering the models computational load. A large separable kernel attention module is employed to improve the spatial pyramid pooling module, enhancing the models feature fusion ability. Experimental results demonstrate that, compared to other models, the proposed model achieves higher detection accuracy, lower missed detection rates, and fewer parameters.

Key words: small target detection, feature fusion, multi-scale attention mechanism, feature map redundancy

CLC Number: 

  • TP391
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