感知增強(qiáng)混合網(wǎng)絡(luò)的水下目標(biāo)檢測

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中圖分類號：TP391 文獻(xiàn)標(biāo)識碼：Adoi：10.37188/0PE.20253308.1303

Abstract：Underwater object detection technology plays an important role in areas of marine resource exploration and environmental protection. However，the problems of blurred imaging and variable object scales in underwater environments pose dificulties fordetection tasks. As a result，it is challenging for accurate underwater object feature extraction，which influences the detection performance of existing methods.To solve the above-mentioned problem，a feature enhanced hybrid network was proposed to improve the detection accuracy of underwater objects.Firstly，a global-local hybrid feature enhancement network was constructed.The long-range global information in the image was extracted via self-attention mechanisms，and the richer localdetailed information was further calculated through the devised convolutional attention enhancement module.The global and local relationships in the images could be beter established， hence the multiscale feature representation powers of the network were enhanced. Subsequently，in order to suppress the interference of imaging blurriness and low contrast in underwater environments，a twostage object perception enhanced detection head was constructed. The depth of the first-stage region proposal network was increased，thus more semantic information of underwater objects could be extracted. Besides，the self-atention mechanism was introduced in the second stage to suppress the interference from background noise.Moreover，an intersection-over-union branch was added to further integratethe prior information of objects obtained from the first stage into the second stage.The proposed method achieves （204號 37.8% ， 61.8% ，and 82.0% ， 98.9% of mAP0.5：0.95 and AP50 on the TrashCan and WPBB datasets respectively. The qualitative and quantitative comparison experimental results demonstrate that this method could obtain superior detection results for various underwater objects.

Key Words：underwater object detection；feature enhancement；self-attention；hybrid network

1引言

利用水下目標(biāo)檢測技術(shù)可以自動定位出水中感興趣目標(biāo)在圖像中的位置，并給出目標(biāo)的類別信息。（剩余14372字）