为提高无人机对架空输电线路巡检的效率和线路中螺栓缺销的检测精度,提出了改进的你只看一次第7微小版(you only look once version 7-tiny,YOLOv7-tiny)输电线路螺栓缺销检测算法。该算法采用高效的分布移位卷积(distribution shifting...为提高无人机对架空输电线路巡检的效率和线路中螺栓缺销的检测精度,提出了改进的你只看一次第7微小版(you only look once version 7-tiny,YOLOv7-tiny)输电线路螺栓缺销检测算法。该算法采用高效的分布移位卷积(distribution shifting convolution,DSConv)来替换YOLOv7-tiny网络中的3×3卷积,以提高模型的计算速度并降低计算复杂度;在模型的检测头部分,添加了高效解耦头结构,以提高模型的准确度和稳定性;并采用明智的交并比(wise intersection over union,WIoU)损失函数来提高正样本的权重,使模型更加关注缺销螺栓目标,以减少正负样本不平衡带来的噪声干扰。实验结果表明,改进YOLOv7-tiny算法对输电线路螺栓缺销检测的平均精度均值达到90.6%,检测速度达到143.0帧/s,同时实现了检测的高速度和高精度。该算法在无人机输电线路巡检中具有一定的优势。展开更多
为了实现对水稻病害的精准检测,文章基于YOLOv8n模型(You Only Look Once version 8 nano)提出了一个全新的改进模型YOLO-Rice。该模型通过3项关键的技术创新,提升了对水稻叶片和稻穗病害的检测精度。首先模型在骨干网络中引入CBAM(Conv...为了实现对水稻病害的精准检测,文章基于YOLOv8n模型(You Only Look Once version 8 nano)提出了一个全新的改进模型YOLO-Rice。该模型通过3项关键的技术创新,提升了对水稻叶片和稻穗病害的检测精度。首先模型在骨干网络中引入CBAM(Convolutional Block Attention Module)卷积注意力机制;其次模型采用Gold-YOLO的GD(Gather-and-Distribute)机制,在模型的颈部进行特征融合;最后更换了传统的损失函数,采用WIoU作为新的损失函数。通过上述改进,YOLO-Rice模型在平均精度均值(mAP 50%)上实现了3.4百分点的显著提升,最终达到了96.0%的准确率,充分证明了YOLO-Rice模型在水稻病害检测任务中的有效性。展开更多
The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Con...The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Convolutional Block Attention Module (CBAM) has been developed. This algorithm initially employs the latest YOLOv8 for object recognition. Subsequently, the integration of CBAM enhances its feature extraction capabilities. Finally, the WIoU function is used to optimize the network’s bounding box loss, facilitating rapid convergence. Experimental validation using a smoke and fire dataset demonstrated that the proposed algorithm achieved a 2.3% increase in smoke and fire detection accuracy, surpassing other state-of-the-art methods.展开更多
文摘为提高无人机对架空输电线路巡检的效率和线路中螺栓缺销的检测精度,提出了改进的你只看一次第7微小版(you only look once version 7-tiny,YOLOv7-tiny)输电线路螺栓缺销检测算法。该算法采用高效的分布移位卷积(distribution shifting convolution,DSConv)来替换YOLOv7-tiny网络中的3×3卷积,以提高模型的计算速度并降低计算复杂度;在模型的检测头部分,添加了高效解耦头结构,以提高模型的准确度和稳定性;并采用明智的交并比(wise intersection over union,WIoU)损失函数来提高正样本的权重,使模型更加关注缺销螺栓目标,以减少正负样本不平衡带来的噪声干扰。实验结果表明,改进YOLOv7-tiny算法对输电线路螺栓缺销检测的平均精度均值达到90.6%,检测速度达到143.0帧/s,同时实现了检测的高速度和高精度。该算法在无人机输电线路巡检中具有一定的优势。
文摘为了实现对水稻病害的精准检测,文章基于YOLOv8n模型(You Only Look Once version 8 nano)提出了一个全新的改进模型YOLO-Rice。该模型通过3项关键的技术创新,提升了对水稻叶片和稻穗病害的检测精度。首先模型在骨干网络中引入CBAM(Convolutional Block Attention Module)卷积注意力机制;其次模型采用Gold-YOLO的GD(Gather-and-Distribute)机制,在模型的颈部进行特征融合;最后更换了传统的损失函数,采用WIoU作为新的损失函数。通过上述改进,YOLO-Rice模型在平均精度均值(mAP 50%)上实现了3.4百分点的显著提升,最终达到了96.0%的准确率,充分证明了YOLO-Rice模型在水稻病害检测任务中的有效性。
文摘The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Convolutional Block Attention Module (CBAM) has been developed. This algorithm initially employs the latest YOLOv8 for object recognition. Subsequently, the integration of CBAM enhances its feature extraction capabilities. Finally, the WIoU function is used to optimize the network’s bounding box loss, facilitating rapid convergence. Experimental validation using a smoke and fire dataset demonstrated that the proposed algorithm achieved a 2.3% increase in smoke and fire detection accuracy, surpassing other state-of-the-art methods.
