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基于改進(jìn)Cascade R-CNN和圖像增強(qiáng)的夜晚魚類檢測
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國家自然科學(xué)基金項(xiàng)目(61702323、61972240)和大洋漁業(yè)資源可持續(xù)開發(fā)教育部重點(diǎn)實(shí)驗(yàn)室開放基金項(xiàng)目(A1-2006-00-301104)


Object Detection of Underwater Fish at Night Based on Improved Cascade R-CNN and Image Enhancement
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    摘要:

    針對光照不均,、噪聲大,、拍攝質(zhì)量不高的夜晚水下環(huán)境,為實(shí)現(xiàn)夜晚水下圖像中魚類目標(biāo)的快速檢測,,利用計(jì)算機(jī)視覺技術(shù),,提出了一種基于改進(jìn)Cascade R-CNN算法和具有色彩保護(hù)的MSRCP(Multi-scale Retinex with color restoration)圖像增強(qiáng)算法的夜晚水下魚類目標(biāo)檢測方法。首先針對夜晚水下環(huán)境的視頻數(shù)據(jù),,根據(jù)時(shí)間間隔,,截取出相應(yīng)的夜晚水下魚類圖像,對截取的原始圖像進(jìn)行MSRCP圖像增強(qiáng),。然后采用DetNASNet主干網(wǎng)絡(luò)進(jìn)行網(wǎng)絡(luò)訓(xùn)練和水下魚類特征信息的提取,,將提取出的特征信息輸入到Cascade R-CNN模型中,并使用Soft-NMS候選框優(yōu)化算法對其中的RPN網(wǎng)絡(luò)進(jìn)行優(yōu)化,,最后對夜晚水下魚類目標(biāo)進(jìn)行檢測,。實(shí)驗(yàn)結(jié)果表明,該方法解決了夜晚水下環(huán)境中的圖像降質(zhì),、魚類目標(biāo)重疊檢測問題,,實(shí)現(xiàn)了對夜晚水下魚類目標(biāo)的快速檢測,對夜晚水下魚類圖像目標(biāo)檢測的查準(zhǔn)率達(dá)到95.81%,,比Cascade R-CNN方法提高了11.57個(gè)百分點(diǎn),。

    Abstract:

    The underwater environment at night has the characteristics of uneven illumination, big noise and low quality of underwater fish video. In view of this, in order to realize the rapid detection of fish targets in underwater images at night, a method of underwater fish object detection at night based on improved Cascade R-CNN algorithm and MSRCP image enhancement algorithm with color protection was proposed by using computer vision technology. Firstly, using the video data of the underwater environment at night, corresponding underwater fish images at night were extracted according to the time interval. The original extracted image was enhanced by MSRCP. Then the DetNASNet backbone network was used to train network model and extract underwater fish feature information. The extracted feature information was input into the Cascade R-CNN model, and the Soft-NMS candidate box optimization algorithm was used to optimize the RPN network. Finally, the underwater fish target at night was detected. The experimental results showed that the method can solve the problems of image degradation and fish object overlapping detection in the underwater environment at night, and realize the rapid detection of underwater fish target at night. Using this method, the accuracy rate of object detection with underwater fish image at night can reach 95.81%, which was 11.57 percentage points higher than that of the traditional Cascade R-CNN method.

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張明華,龍騰,宋巍,黃冬梅,梅海彬,賀琪.基于改進(jìn)Cascade R-CNN和圖像增強(qiáng)的夜晚魚類檢測[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2021,52(9):179-185. ZHANG Minghua, LONG Teng, SONG Wei, HUANG Dongmei, MEI Haibin, HE Qi. Object Detection of Underwater Fish at Night Based on Improved Cascade R-CNN and Image Enhancement[J]. Transactions of the Chinese Society for Agricultural Machinery,2021,52(9):179-185.

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  • 收稿日期:2020-10-08
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  • 在線發(fā)布日期: 2021-09-10
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