基于改进Yolov5的遥感光伏检测算法

Taget Detection of Photovoltatic Remote Sensing Based on Improved Yolov5 Model

针对遥感光伏图像分辨率高、环境噪声较大以及背景复杂等问题,提出了一种改进Yolov5目标检测模型,以实现对光伏电厂的定位。首先,在主干特征提取网络的卷积层中添加CA(Coordinate Attention)坐标注意力机制提高网络特征的学习能力;其次,将Ghostconv网络结构加入到Backbone中,用Ghostconv网络模块替换Conv网络模块;设计新的GhostC3网络代替原来的C3网络模块,提高模型的学习效率;最后,将损失函数由GIoU_Loss函数改为SIoU_Loss函数。实验结果表明,相比原Yolov5方法,改进算法的平均精度均值mAP、精准率和召回率分别达到了97.5%、98.9%和94.9%,提升了1.8%、1.7%和5.8%,验证了该算法对光伏检测具有很好的效果。

Aiming at high-sensing photovoltaic image resolution,high environmental noise,and complex background,an improved Yolov5 model is proposed to achieve positioning of photovoltaic power plants.First of all,the CA(Coordinate Attention) mechanism is added to the compassionate layer of the main feature extraction network to improve the learning ability of the network characteristics;second,the Ghostconv network structure is added to Backbone,useing the Ghostconv network module to replace the Conv network module,designing a new GhostC3 network network instead of the original C3 network module to improve the learning efficiency of the model;finally,the GIoU_Loss function is changed to the SIoU_Loss function.Compared with the original Yolov5 method,the average accuracy of the improved algorithm mAP,accuracy,and recall rate reached 97.5%,98.9%,and 94.9%,respectively,which have increased by 1.8%,1.7%,and 5.8%,respectively.The algorithm has a good effect on photovoltaic detection.