在遮挡图像中,行人目标通常被其他物体部分或完全遮挡,导致其外观特征不完整、边缘模糊,甚至与背景或遮挡物混淆。行人遮挡目标的检测需要算法能够在特征缺失的情况下,仍然准确识别和定位目标。为了解决这一挑战,本文基于YOLOv10提出一...在遮挡图像中,行人目标通常被其他物体部分或完全遮挡,导致其外观特征不完整、边缘模糊,甚至与背景或遮挡物混淆。行人遮挡目标的检测需要算法能够在特征缺失的情况下,仍然准确识别和定位目标。为了解决这一挑战,本文基于YOLOv10提出一种融合多尺度自注意力机制(Efficient Multi-directional Self-Attention, EMSA)的多尺度感知能力的YOLOv10改进方法。首先在YOLOv10中的C2f中融合MSDA注意力机制,增强了模型在多尺度上的特征捕捉能力,提升了对不同尺度遮挡目标的检测能力,通过自适应地加权不同通道的特征,提高了对遮挡目标特征的关注;其次基于动态聚焦机制引入新的损失函数Focaleriou,动态调整损失焦点,提高对不同尺度目标的检测能力,同时改善边界框回归损失收敛速度,之后添加了小目标检测头,增强小遮挡目标的特征提取能力;最后使用公开数据集Citypersons进行消融实验。结果表明,该融合了MSDA注意力机制的模型平均精度(Map@0.5)达到了62.3%,相较于官方YOLOv10n提升了2.2%。实验结果表明该EMSA注意力能够有效改进行人遮挡目标的检测,满足自动驾驶、监控等应用场景下的行人遮挡场景的检测需求。In occluded images, pedestrian targets are often partially or completely blocked by other objects, leading to incomplete appearance features, blurred edges, and even confusion with the background or occluding objects. Detecting occluded pedestrian targets requires algorithms capable of accurately recognizing and localizing targets despite missing features. To address this challenge, this paper proposes an improved YOLOv10 method with enhanced multi-scale perception by integrating an Efficient Multi-directional Self-Attention (EMSA) mechanism. Firstly, the MSDA attention mechanism is incorporated into the C2f module of YOLOv10 to enhance the model’s ability to capture features at multiple scales, improving the detection of occluded targets of various sizes. By adaptively weighting features across channels, the method increases focus on occluded target features. Secondly, a novel loss function, Focaleriou, is introduced based on a dynamic focusing mechanism. This adjusts the focus of the loss dynamically, enhancing the detection of targets at different scales and improving the convergence speed of bounding box regression loss. Additionally, a small-object detection head is added to strengthen feature extraction for small occluded targets. Finally, ablation experiments are conducted on the public Citypersons dataset. Results show that the model incorporating the MSDA attention mechanism achieves a mean average precision (mAP@0.5) of 62.3%, which is 2.2% higher than the official YOLOv10n. Experimental findings demonstrate that the EMSA attention mechanism effectively improves the detection of occluded pedestrian targets, meeting the requirements for scenarios such as autonomous driving and surveillance under occluded pedestrian conditions.展开更多
【目的】克隆强抗寒性牧草——短芒大麦DREB1(dehydration responsive element binding protein 1)转录因子,分析其生理生化特性,为理想抗逆工程基因的筛选和利用奠定理论基础。【方法】利用RACE-PCR(Rapidamplification of cDNAends-po...【目的】克隆强抗寒性牧草——短芒大麦DREB1(dehydration responsive element binding protein 1)转录因子,分析其生理生化特性,为理想抗逆工程基因的筛选和利用奠定理论基础。【方法】利用RACE-PCR(Rapidamplification of cDNAends-polymerase chain reaction)技术分离短芒大麦DREB1转录因子全长cDNA序列,North-ern杂交和凝胶滞留试验分析其在逆境条件下的表达情况,及其与DRE(dehydration responsive element)元件的结合活性。【结果】从强抗寒性短芒大麦中成功分离了1个新的DREB1类转录因子HbDREB1,该基因全长899 bp,其蛋白序列中含有1个典型的AP2/EREBP DNA结构域及"PKK/RPAGRxKFxETRHP"和"DSAWR"、"LWSY"3个DREB1特征标签序列;序列比对分析表明,HbDREB1与其他植物的DREB1类转录因子的同源性较高。HbDREB1在转录水平上明显受冷胁迫诱导表达,具有结合DRE-顺式作用元件的功能及作为转录因子必备的核定位特性。【结论】HbDREB1基因参与了非生物胁迫信号转导,具有提高植物抗寒性的潜能。展开更多
文摘在遮挡图像中,行人目标通常被其他物体部分或完全遮挡,导致其外观特征不完整、边缘模糊,甚至与背景或遮挡物混淆。行人遮挡目标的检测需要算法能够在特征缺失的情况下,仍然准确识别和定位目标。为了解决这一挑战,本文基于YOLOv10提出一种融合多尺度自注意力机制(Efficient Multi-directional Self-Attention, EMSA)的多尺度感知能力的YOLOv10改进方法。首先在YOLOv10中的C2f中融合MSDA注意力机制,增强了模型在多尺度上的特征捕捉能力,提升了对不同尺度遮挡目标的检测能力,通过自适应地加权不同通道的特征,提高了对遮挡目标特征的关注;其次基于动态聚焦机制引入新的损失函数Focaleriou,动态调整损失焦点,提高对不同尺度目标的检测能力,同时改善边界框回归损失收敛速度,之后添加了小目标检测头,增强小遮挡目标的特征提取能力;最后使用公开数据集Citypersons进行消融实验。结果表明,该融合了MSDA注意力机制的模型平均精度(Map@0.5)达到了62.3%,相较于官方YOLOv10n提升了2.2%。实验结果表明该EMSA注意力能够有效改进行人遮挡目标的检测,满足自动驾驶、监控等应用场景下的行人遮挡场景的检测需求。In occluded images, pedestrian targets are often partially or completely blocked by other objects, leading to incomplete appearance features, blurred edges, and even confusion with the background or occluding objects. Detecting occluded pedestrian targets requires algorithms capable of accurately recognizing and localizing targets despite missing features. To address this challenge, this paper proposes an improved YOLOv10 method with enhanced multi-scale perception by integrating an Efficient Multi-directional Self-Attention (EMSA) mechanism. Firstly, the MSDA attention mechanism is incorporated into the C2f module of YOLOv10 to enhance the model’s ability to capture features at multiple scales, improving the detection of occluded targets of various sizes. By adaptively weighting features across channels, the method increases focus on occluded target features. Secondly, a novel loss function, Focaleriou, is introduced based on a dynamic focusing mechanism. This adjusts the focus of the loss dynamically, enhancing the detection of targets at different scales and improving the convergence speed of bounding box regression loss. Additionally, a small-object detection head is added to strengthen feature extraction for small occluded targets. Finally, ablation experiments are conducted on the public Citypersons dataset. Results show that the model incorporating the MSDA attention mechanism achieves a mean average precision (mAP@0.5) of 62.3%, which is 2.2% higher than the official YOLOv10n. Experimental findings demonstrate that the EMSA attention mechanism effectively improves the detection of occluded pedestrian targets, meeting the requirements for scenarios such as autonomous driving and surveillance under occluded pedestrian conditions.
文摘【目的】克隆强抗寒性牧草——短芒大麦DREB1(dehydration responsive element binding protein 1)转录因子,分析其生理生化特性,为理想抗逆工程基因的筛选和利用奠定理论基础。【方法】利用RACE-PCR(Rapidamplification of cDNAends-polymerase chain reaction)技术分离短芒大麦DREB1转录因子全长cDNA序列,North-ern杂交和凝胶滞留试验分析其在逆境条件下的表达情况,及其与DRE(dehydration responsive element)元件的结合活性。【结果】从强抗寒性短芒大麦中成功分离了1个新的DREB1类转录因子HbDREB1,该基因全长899 bp,其蛋白序列中含有1个典型的AP2/EREBP DNA结构域及"PKK/RPAGRxKFxETRHP"和"DSAWR"、"LWSY"3个DREB1特征标签序列;序列比对分析表明,HbDREB1与其他植物的DREB1类转录因子的同源性较高。HbDREB1在转录水平上明显受冷胁迫诱导表达,具有结合DRE-顺式作用元件的功能及作为转录因子必备的核定位特性。【结论】HbDREB1基因参与了非生物胁迫信号转导,具有提高植物抗寒性的潜能。
