Wetland waterbirds serve as key ecological indicators for assessing habitat quality and biodiversity.Accurate identification of waterbird species is a cornerstone of long-term ecological monitoring.The resulting data ...Wetland waterbirds serve as key ecological indicators for assessing habitat quality and biodiversity.Accurate identification of waterbird species is a cornerstone of long-term ecological monitoring.The resulting data are critical for assessing wetland ecosystem health and biodiversity.However,prevailing recognition approaches often prioritize detection accuracy at the expense of computational efficiency.They are also hindered by complex background heterogeneity and interspecies visual similarity.These limitations hinder the scalability and practical deployment of such methods for on-site ecological monitoring within wetland ecosystems.To address these challenges,this study proposes an optimized end-to-end framework,ShuffleNetV2-iRMB-ShapeIoU-YOLO(SISYOLO),designed for robust recognition of wetland waterbirds in complex environments.Specifically,the proposed framework integrates ShuffleNetV2 with inverted Residual Mobile Blocks(iRMB) to improve computational efficiency while maintaining robust feature representation.This design further enables deployment on resource-constrained mobile and embedded platforms.Additionally,ShapeIoU,a refined bounding box similarity metric,is introduced to jointly optimize overlap and shape consistency,effectively mitigating misclassification among visually similar species.Experimental results on the IC-Beijing dataset show that SIS-YOLO achieves 91.1% precision and 79.1% mAP@0.5:0.95 with only 2.9 million parameters.Compared with the lightweight baseline YOLOv8n,it improves precision by 2% and mAP@0.5:0.95 by 1.2%,while requiring fewer parameters and offering higher computational efficiency.展开更多
苹果作为全球重要的经济作物之一,其产量和质量直接受到病害的影响。为解决目前传统人工检测病害过程中主观性强、效率低,以及农业检测设备资源有限等问题,基于YOLOv8(You Only Look Once Version 8)提出了一种高效、轻量化的病害检测...苹果作为全球重要的经济作物之一,其产量和质量直接受到病害的影响。为解决目前传统人工检测病害过程中主观性强、效率低,以及农业检测设备资源有限等问题,基于YOLOv8(You Only Look Once Version 8)提出了一种高效、轻量化的病害检测模型—YOLOv8-RIC。在YOLOv8的基础上对主干网络进行了优化,分别引入了轻量级卷积神经网络RGN(RepGhostNet)和改进型残差移动网络iRMB(Improved Residual MobileNet Backbone),替换了原有的C2f模块,有效提升了模型的特征提取能力并降低了硬件计算成本。与原始YOLOv8模型相比,YOLOv8-RIC在自建图像数据集上的目标检测任务中,mAP(多类别平均精度)提高了6.2%,Precision(精确度)提高了12.7%。实验结果表明,该方法在复杂场景下对苹果树病害的检测具有较高的效率和鲁棒性,为精准农业的发展提供了有力支持。展开更多
基金supported by National Natural Science Foundation of China (32401569,32371874)Beijing Natural Science Foundation(6244053)。
文摘Wetland waterbirds serve as key ecological indicators for assessing habitat quality and biodiversity.Accurate identification of waterbird species is a cornerstone of long-term ecological monitoring.The resulting data are critical for assessing wetland ecosystem health and biodiversity.However,prevailing recognition approaches often prioritize detection accuracy at the expense of computational efficiency.They are also hindered by complex background heterogeneity and interspecies visual similarity.These limitations hinder the scalability and practical deployment of such methods for on-site ecological monitoring within wetland ecosystems.To address these challenges,this study proposes an optimized end-to-end framework,ShuffleNetV2-iRMB-ShapeIoU-YOLO(SISYOLO),designed for robust recognition of wetland waterbirds in complex environments.Specifically,the proposed framework integrates ShuffleNetV2 with inverted Residual Mobile Blocks(iRMB) to improve computational efficiency while maintaining robust feature representation.This design further enables deployment on resource-constrained mobile and embedded platforms.Additionally,ShapeIoU,a refined bounding box similarity metric,is introduced to jointly optimize overlap and shape consistency,effectively mitigating misclassification among visually similar species.Experimental results on the IC-Beijing dataset show that SIS-YOLO achieves 91.1% precision and 79.1% mAP@0.5:0.95 with only 2.9 million parameters.Compared with the lightweight baseline YOLOv8n,it improves precision by 2% and mAP@0.5:0.95 by 1.2%,while requiring fewer parameters and offering higher computational efficiency.
文摘苹果作为全球重要的经济作物之一,其产量和质量直接受到病害的影响。为解决目前传统人工检测病害过程中主观性强、效率低,以及农业检测设备资源有限等问题,基于YOLOv8(You Only Look Once Version 8)提出了一种高效、轻量化的病害检测模型—YOLOv8-RIC。在YOLOv8的基础上对主干网络进行了优化,分别引入了轻量级卷积神经网络RGN(RepGhostNet)和改进型残差移动网络iRMB(Improved Residual MobileNet Backbone),替换了原有的C2f模块,有效提升了模型的特征提取能力并降低了硬件计算成本。与原始YOLOv8模型相比,YOLOv8-RIC在自建图像数据集上的目标检测任务中,mAP(多类别平均精度)提高了6.2%,Precision(精确度)提高了12.7%。实验结果表明,该方法在复杂场景下对苹果树病害的检测具有较高的效率和鲁棒性,为精准农业的发展提供了有力支持。
