改进YOLOv11的水面膨化饲料颗粒图像实时检测算法  

Real-time Detection Algorithm of Expanded Feed Image on the Water Surface Based on Improved YOLOv11

作　　者：周秀珊 文露婷 介百飞 郑海锋 吴其琦[1] 李克讷[1] 梁军能 黎一键 文家燕[1] 江林源 ZHOU Xiushan;WEN Luting;JIE Baifei;ZHENG Haifeng;WU Qiqi;LI Kene;LIANG Junneng;LI Yijian;WEN Jiayan;JIANG Linyuan(Automation College,Guangxi University of Science and Technology,Liuzhou 545006,China;Guangxi Academy of Fishery Sciences,Nanning 530021,China;Aquatic Technology Promotion Station of Guangxi Zhuang Autonomous Region,Nanning 530022,China)

机构地区：[1]广西科技大学自动化学院,广西柳州545006 [2]广西壮族自治区水产科学研究院,广西南宁530021 [3]广西壮族自治区水产技术推广站,广西南宁530022

出　　处：《智慧农业(中英文)》2024年第6期155-167,共13页Smart Agriculture

基　　金：广西重点研发计划项目(桂科AB21220019);国家现代农业产业技术体系广西虾类贝类产业创新团队首席专家项目(nycytxgxcxtd-2023-14-01);水产产业科技先锋队项目(桂农科盟202410)。

摘　　要：[目的/意义]针对水面膨化饲料的图像在水产养殖水体中存在水体浑浊导致饲料与背景对比不明显、光照不均匀、鱼群抢食引起的水花导致饲料重叠粘连以及增氧设备产生的气泡遮挡饲料成像等问题,提出一种高效的水面膨化饲料图像检测YOLOv11-AP2S模型,为水产集约化养殖模式下的智能投喂决策提供准确依据。[方法]在YOLOv11的骨干网络的第10层C2PSA后增加细粒度分类的注意力机制(Attention for Fine-Grained Categoriza⁃tion,AFGC),将C3k2模块替换为VoV-GSCSP模块,以及在YOLOv11的基础上增加P2层。为了保持模型的实时性,在P2层使用轻量级的VoV-GSCSP模块进行特征融合。在不降低检测速度和不损失模型轻量化程度的情况下提高检测精度,提出YOLOv11-AP2S水面膨化饲料实时检测模型。[结果与讨论]实验结果显示,YOLOv11-AP2S模型在识别精确度、召回率上均达到了78.70%,IoU阈值为0.5时的平均精度值(mAP50)高达80.00%,F1分数也达到了79.00%。与原YOLOv11网络相比,这些指标分别提高了提高6.7个百分点、9.0个百分点、9.4个百分点和8.0个百分点。与其他YOLO模型相比,YOLOv11-AP2S模型在自制数据集上的检测结果也具有明显优势,且在同等迭代次数下具有更高的平均精度均值和更低的损失。[结论]YOLOv11-AP2S模型能够通过摄像头对水面膨化饲料颗粒的剩余情况进行实时检测,进而实现对鱼群摄食行为的准确观测与分析,为智慧渔业精准投喂的研究和应用提供有力支持。[Objective]During the feeding process of fish populations in aquaculture,the video image characteristics of floating extruded feed on the water surface undergo continuous variations due to a myriad of environmental factors and fish behaviors.These variations pose significant challenges to the accurate detection of feed particles,which is crucial for effective feeding management.To address these challenges and enhance the detection of floating extruded feed particles on the water surface,,thereby providing precise decision support for intelligent feeding in intensive aquaculture modes,the YOLOv11-AP2S model,an advanced detection model was proposed.[Methods]The YOLOv11-AP2S model enhanced the YOLOv11 algorithm by incorporating a series of improvements to its backbone network,neck,and head components.Specifically,an attention for fine-grained categorization(AFGC)mechanism was introduced after the 10th layer C2PSA of the backbone network.This mechanism aimed to boost the model's capability to capture fine-grained features,which were essential for accurately identifying feed particles in complex environments with low contrast and overlapping objects.Furthermore,the C3k2 module was replaced with the VoV-GSCSP module,which incorporated more sophisticated feature extraction and fusion mechanisms.This replacement further enhanced the network's ability to extract relevant features and improve detection accuracy.To improve the model's detection of small targets,a P2 layer was introduced.However,adding a P2 layer may increase computational complexity and resource consumption,so the overall performance and resource consumption of the model must be carefully balanced.To maintain the model's real-time performance while improving detection accuracy,a lightweight VoV-GSCSP module was utilized for feature fusion at the P2 layer.This approach enabled the YOLOv11-AP2S model to achieve high detection accuracy without sacrificing detection speed or model lightweights,making it suitable for real-time applications in aquaculture.[Result

关 键 词：鱼群摄食行为 膨化饲料 水产养殖 YOLOv11 实时检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] S964.7[自动化与计算机技术—计算机科学与技术]