Aiming at the problem of low detection accuracy due to the different scale sizes of apple leaf disease spots and their similarity to the background,this paper proposes a multi-scale lightweight network(MSL-Net).Firstl...Aiming at the problem of low detection accuracy due to the different scale sizes of apple leaf disease spots and their similarity to the background,this paper proposes a multi-scale lightweight network(MSL-Net).Firstly,a multiplexed aggregated feature extraction network is proposed using residual bottleneck block(RES-Bottleneck)and middle partial-convolution(MP-Conv)to capture multi-scale spatial features and enhance focus on disease features for better differentiation between disease targets and background information.Secondly,a lightweight feature fusion network is designed using scale-fuse concatenation(SF-Cat)and triple-scale sequence feature fusion(TSSF)module to merge multi-scale feature maps comprehensively.Depthwise convolution(DWConv)and GhostNet lighten the network,while the cross stage partial bottleneck with 3 convolutions ghost-normalization attention module(C3-GN)reduces missed detections by suppressing irrelevant background information.Finally,soft non-maximum suppression(Soft-NMS)is used in the post-processing stage to improve the problem of misdetection of dense disease sites.The results show that the MSL-Net improves mean average precision at intersection over union of 0.5(mAP@0.5)by 2.0%over the baseline you only look once version 5s(YOLOv5s)and reduces parameters by 44%,reducing computation by 27%,outperforming other state-of-the-art(SOTA)models overall.This method also shows excellent performance compared to the latest research.展开更多
文摘Aiming at the problem of low detection accuracy due to the different scale sizes of apple leaf disease spots and their similarity to the background,this paper proposes a multi-scale lightweight network(MSL-Net).Firstly,a multiplexed aggregated feature extraction network is proposed using residual bottleneck block(RES-Bottleneck)and middle partial-convolution(MP-Conv)to capture multi-scale spatial features and enhance focus on disease features for better differentiation between disease targets and background information.Secondly,a lightweight feature fusion network is designed using scale-fuse concatenation(SF-Cat)and triple-scale sequence feature fusion(TSSF)module to merge multi-scale feature maps comprehensively.Depthwise convolution(DWConv)and GhostNet lighten the network,while the cross stage partial bottleneck with 3 convolutions ghost-normalization attention module(C3-GN)reduces missed detections by suppressing irrelevant background information.Finally,soft non-maximum suppression(Soft-NMS)is used in the post-processing stage to improve the problem of misdetection of dense disease sites.The results show that the MSL-Net improves mean average precision at intersection over union of 0.5(mAP@0.5)by 2.0%over the baseline you only look once version 5s(YOLOv5s)and reduces parameters by 44%,reducing computation by 27%,outperforming other state-of-the-art(SOTA)models overall.This method also shows excellent performance compared to the latest research.
文摘与通用目标检测不同,无人机(Unmanned Aerial Vehicle,UAV)航拍图像目标检测主要面临两个难题:(1)远距离观察下存在大量小尺寸目标,难以与背景区分;(2)大量区域中目标密集且存在严重遮挡.因此,将通用目标检测器直接应用于航拍图像会导致检测精度下降.本文提出一种聚焦小目标的航拍图像目标检测算法(Focusing on Small objects Detector in aerial images,FocSDet).针对小目标,通过密集高级组合(Dense Higher-Level Composition,DHLC)模式连接双Swin-Transfomer骨干网络,并和特征金字塔(Feature Pyramid Networks,FPN)结合,构建小目标特征聚合网络作为FocSDet的骨干网络,可丰富单层特征表达并提升对图像全局信息的利用,在不损失大目标语义信息的同时得到对小目标更好的特征描述,有效提升了小目标检测能力;针对区域密集遮挡,提出任务平衡样本分配策略,区别于现有样本分配策略只依赖定位位置,本文所提出的策略中样本匹配质量评价分数由定位位置信息和预测分类分数共同构成.基于该新评价分数不断迭代更新样本分配和监督网络优化,取得了更高质量的预测结果.最后,在检测头的分类和回归分支中引入层注意力构成增强检测头,进一步提升了小目标的检测性能.在Visdrone无人机数据集、CARPK航拍数据集上的实验表明,本文提出的FocSDet相较于现有方法ATSS和VFNET,在Visdrone上平均精度(Average Precision,AP)分别提升2%和0.6%,小目标APs分别提升2.6%和1.2%;在CARPK上AP分别提升2.2%和1.7%,小目标APs分别提升5.2%和5.0%.
