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海洋環(huán)境下基于增強(qiáng)YOLOv7的垃圾目標(biāo)檢測(cè)
電子技術(shù)應(yīng)用
廖辰津
福建理工大學(xué)
摘要: 針對(duì)海洋垃圾識(shí)別任務(wù)在實(shí)際應(yīng)用中模型準(zhǔn)確率不高的問題,,提出一種基于優(yōu)化YOLOv7的海洋垃圾識(shí)別算法,。在圖像增強(qiáng)部分,,基于概率UIE的框架,,通過添加eSE注意力減少特征信息的丟失,。在損失函數(shù)部分,,在IoU損失函數(shù)的基礎(chǔ)上引入兩層注意力機(jī)制的損失函數(shù),,將其與EIoU損失函數(shù)融合進(jìn)一步提升模型的泛化能力,。將該算法應(yīng)用于海洋垃圾檢測(cè)任務(wù),,并在基礎(chǔ)數(shù)據(jù)集上對(duì)其進(jìn)行評(píng)估,。在YOLOTrashCan兩個(gè)數(shù)據(jù)集上的平均精度均值指標(biāo)分別達(dá)到69.5%、63.5%,,相較于YOLOv7算法分別提升6%,、1.6%。整體實(shí)驗(yàn)結(jié)果表明,,所構(gòu)建的算法能有效提升海洋垃圾檢測(cè)的準(zhǔn)確性,。
中圖分類號(hào):TP391.41 文獻(xiàn)標(biāo)志碼:A DOI: 10.16157/j.issn.0258-7998.244869
中文引用格式: 廖辰津. 海洋環(huán)境下基于增強(qiáng)YOLOv7的垃圾目標(biāo)檢測(cè)[J]. 電子技術(shù)應(yīng)用,2024,,50(6):66-70.
英文引用格式: Liao Chenjin. Garbage object detection based on enhanced YOLOv7 in marine environment[J]. Application of Electronic Technique,,2024,50(6):66-70.
Garbage object detection based on enhanced YOLOv7 in marine environment
Liao Chenjin
Fujian University of Technology
Abstract: To address the issue of low model accuracy in practical applications of marine debris identification, this paper proposes an improved garbage classification algorithm based on optimized YOLOv7. In the image enhancement part, a probabilistic UIE framework is introduced to reduce the loss of feature information by incorporating eSE attention. In the loss function part, a two-layer attention mechanism is added to the IoU loss function to enhance the model’s generalization ability when combined with the EIoU loss function. The proposed algorithm is applied to marine debris detection tasks and evaluated on benchmark datasets. The average precision on the YOLOTrashCan datasets achieves 69.5% and 63.5%, respectively, representing a 6% and 1.6% improvement compared to the YOLOv7 algorithm. Overall experimental results demonstrate that the algorithm constructed in this paper effectively enhances the accuracy of marine debris detection.
Key words : EUIE;eSE attention,;marine debris detection

引言

海洋是地球上最大的生態(tài)系統(tǒng),,其重要性不可低估。隨著人類社會(huì)對(duì)海洋的探索,,人類制造越來越多的垃圾通過各種途徑進(jìn)入海洋并滯留在海洋中,。尤其是海洋織物垃圾,這種海洋垃圾具有持久性與不可分解性,。因此,,清理海洋垃圾刻不容緩。

近年來,,YOLO系列深度學(xué)習(xí)算法在實(shí)際工程中獲得了廣泛應(yīng)用,。鑒于海洋目標(biāo)檢測(cè)在實(shí)際應(yīng)用中的需求,本文以YOLOv7[1]為基礎(chǔ)框架,。


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作者信息:

廖辰津

(福建理工大學(xué),,福建 福州 350118)


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