基于改进YOLOv5s的电厂捞渣机状态异常检测

Abnormal status detection of power plant slag extractor based on im⁃proved YOLOv5s

针对电厂生产作业现场光照条件受限、背景复杂这一现状,为了保障捞渣机的安全高效运行,提出了一种改进YOLOv5s的捞渣机异常状态检测方法。该方法主要是在YOLOv5s网络的基础上,引入ShuffleNet替换原有的主干网络,通过减少网络参数来实现网络的轻量化;同时在ShuffleNet中加入改进的卷积注意力模块,通过串联空间和通道注意力机制,对捞渣机刮板目标特征给予更多的关注;引入加权双向特征金字塔BiFPN和边框回归损失SIoU函数获取特征信息更为有效的特征图提升目标检测精度。研究结果表明,改进后的模型参数量显著减少,模型体积减小了15.2%,平均精确率均值mAP提升了2.2%,检测时间下降了58.0%。在确保检测准确率的同时,实现了对捞渣机异常状态的实时准确检测。

In response to the severe lighting conditions and complex background in the production site of power plant,an improved YOLOv5s abnormal state detection method for slag extractor was proposed to ensure the safe and efficient operation of slag extractor in complex power plant environments.On the basis of YOLOv5s network,the ShuffleNet was introduced to replace the original backbone network and to achieve network lightweight by reducing network parameters.At the same time,an improved convolutional attention module was added to the ShuffleNet,and more attention was paid to the target features of the slag extractor scraper by concatenating space and channel attention mechanisms.The weighted bidirectional feature pyramid Bi-FPN(Bilateral Feature Pyramid Network)and bounding box regression loss SIoU(Scaled IoU)function were introduced to obtain more effective feature maps for feature information to improve target detection accuracy.The research results show that the improved model significantly reduces the number of parameters,reduces the model volume by 15.2%,improves the average accuracy of mAP(mean Average Precision)by 2.2%,and reduces detection time by 58.0%.While ensuring detection accuracy,real-time and accurate detection of abnormal states of the slag extractor is achieved.