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海洋環(huán)境下基于增強YOLOv7的垃圾目標檢測
電子技術(shù)應用
廖辰津
福建理工大學(xué)
摘要: 針對海洋垃圾識別任務(wù)在實(shí)際應用中模型準確率不高的問(wèn)題,提出一種基于優(yōu)化YOLOv7的海洋垃圾識別算法。在圖像增強部分,基于概率UIE的框架,通過(guò)添加eSE注意力減少特征信息的丟失。在損失函數部分,在IoU損失函數的基礎上引入兩層注意力機制的損失函數,將其與EIoU損失函數融合進(jìn)一步提升模型的泛化能力。將該算法應用于海洋垃圾檢測任務(wù),并在基礎數據集上對其進(jìn)行評估。在YOLOTrashCan兩個(gè)數據集上的平均精度均值指標分別達到69.5%、63.5%,相較于YOLOv7算法分別提升6%、1.6%。整體實(shí)驗結果表明,所構建的算法能有效提升海洋垃圾檢測的準確性。
關(guān)鍵詞: EUIE eSE注意力 海洋垃圾檢測
中圖分類(lèi)號:TP391.41 文獻標志碼: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YOLOv7的垃圾目標檢測[J]. 電子技術(shù)應用,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,其重要性不可低估。隨著(zhù)人類(lèi)社會(huì )對海洋的探索,人類(lèi)制造越來(lái)越多的垃圾通過(guò)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(lái),YOLO系列深度學(xué)習算法在實(shí)際工程中獲得了廣泛應用。鑒于海洋目標檢測在實(shí)際應用中的需求,本文以YOLOv7[1]為基礎框架。


本文詳細內容請下載:

http://www.ihrv.cn/resource/share/2000006033


作者信息:

廖辰津

(福建理工大學(xué),福建 福州 350118)


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