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基于優(yōu)化SSD算法的冰鮮鯧魚新鮮度評(píng)估方法研究
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國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2020YFD0900204)和廣東省重點(diǎn)領(lǐng)域研發(fā)計(jì)劃項(xiàng)目(2020B0202010009)


Evaluation Method of Iced Pomfret Freshness Based on Improved SSD
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    摘要:

    保障冰鮮水產(chǎn)品的質(zhì)量安全是提升水產(chǎn)行業(yè)供求效益的關(guān)鍵環(huán)節(jié)之一,冷鏈儲(chǔ)運(yùn)的發(fā)展急需一種快速無(wú)損的魚肉品質(zhì)檢測(cè)技術(shù)。以冰鮮鯧魚為研究對(duì)象,提出用于鯧魚新鮮度質(zhì)變敏感區(qū)域定位與評(píng)估的目標(biāo)檢測(cè)網(wǎng)絡(luò)SSD優(yōu)化方法。首先,建立冰鮮鯧魚新鮮度目標(biāo)檢測(cè)數(shù)據(jù)集。其次,依據(jù)先驗(yàn)知識(shí),以鯧魚的魚眼和魚鰓作為感興趣區(qū)域,基于SSD目標(biāo)檢測(cè)算法自動(dòng)定位與識(shí)別圖像中的質(zhì)變敏感區(qū)域,構(gòu)建鯧魚新鮮度評(píng)估目標(biāo)檢測(cè)模型,通過改進(jìn)主干網(wǎng)絡(luò)和設(shè)計(jì)自適應(yīng)先驗(yàn)框提升網(wǎng)絡(luò)性能。優(yōu)化后的SSD網(wǎng)絡(luò)在金鯧魚和銀鯧魚數(shù)據(jù)集上的平均檢測(cè)精度均值分別達(dá)到98.97%和99.42%,檢測(cè)速度達(dá)到37幀/s。

    Abstract:

    Ensuring the quality and safety of iced aquatic products is the key to improve the benefits of the aquatic industry. Traditional aquatic product freshness evaluation faces the following challenges: complicated operations, samples destruction and low efficiency. An effective and scientific method is urgently needed for aquatic products cold chain system. To solve the above problems, an improved object detection network SSD was proposed to realize the sensitive area location and pomfret freshness evaluation. Firstly, image datasets of iced pomfret freshness grade was established based on environmental factors-pomfret image-total volatile basic nitrogen (TVB-N). The image data of pomfret was collected according to the physicochemical index TVB-N of pomfret freshness at a constant temperature of 0℃, with days as the unit of time. Then, the samples of pomfret images were expanded with data augmentation and marked by LabelImg, annotated image datasets of iced pomfret were provided for freshness detection. Secondly, based on prior knowledge, the eyes and gills of the pomfret were chosen as region of interest. Considering the trade-off between detection accuracy and speed in cold chain application, one-stage object detection network SSD performed better. SSD significantly improved the performance by replacing the backbone network and designing adaptive prior boxes. The improved SSD reached mean average precision of 98.97% and 99.42% on the golden and silver pomfret datasets respectively and the detection speed reached 37 frames per second. The results met the demand for real-time and assessment accuracy in application scenarios, and enabled low-cost, efficient and accurate assessment of pomfret freshness.

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李振波,李 萌,吳宇峰,趙遠(yuǎn)洋,郭若皓,陳雅茹.基于優(yōu)化SSD算法的冰鮮鯧魚新鮮度評(píng)估方法研究[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2021,52(S0):472-481. LI Zhenbo, LI Meng, WU Yufeng, ZHAO Yuanyang, GUO Ruohao, CHEN Yaru. Evaluation Method of Iced Pomfret Freshness Based on Improved SSD[J]. Transactions of the Chinese Society for Agricultural Machinery,2021,52(S0):472-481.

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  • 收稿日期:2021-07-13
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  • 在線發(fā)布日期: 2021-11-10
  • 出版日期: 2021-12-10