针对全球储油罐检测的TCS-YOLO模型

TCS-YOLO model for global oil storage tank inspection

原油作为一种重要的战略物资,在我国经济和军事等多个领域均起到重要作用。本文提出一种基于深度学习的目标检测模型TCS-YOLO(Transformer-CBAM-SIoU YOLO),该模型在YOLOv5的基础上进行优化,同时基于吉林一号光学遥感卫星影像数据集进行实验,对全球范围内的储油罐进行识别与分类。优化内容包括:添加基于Transformer架构的C3TR层对网络进行优化;使用CBAM(Convolutional Block Attention Module)在网络层中添加注意力机制;使用SIoU(Scale-Sensitive Intersection over Union) loss代替CIoU(Complete Intersection over Union) loss作为定位损失函数。实验结果表明:与YOLOv5相比,TCS-YOLO的模型复杂度(Giga Floating Point of Operations,GFLOPs)平均减少3.13%,模型参数量(Parameters)平均减少0.88%,推理速度(Inference Speed)平均降低0.2 ms,mAP0.5(mean Average Precision)平均提升0.2%,mAP0.5∶0.95平均提升1.26%。与此同时,将TCS-YOLO模型与通用目标识别模型YOLOv3,YOLOv4,YOLOv5和Swin Transformer进行对比实验,TCS-YOLO均体现出了更高效的特点。TCS-YOLO模型对全球储油罐的目标识别具有通用可行性,可为遥感数据在能源期货领域提供技术参考。

As a critical strategic resource, crude oil plays a key role in many fields. In particular, it is important to the Chinese economy and military. In this study, we propose a target detection model called Transformer-CBAM-SIoU YOLO(TCS-YOLO) based on YOLOv5. The proposed model was implemented and trained to identify and classify oil storage tanks using the Jilin-1 dataset of optical remote sensing satellite images. The proposed model includes an additional C3TR layer based on the Transformer architecture to optimize the network, as well as a Convolutional Block Attention Module(CBAM) to add an attention mechanism to the network layers. Moreover, we adopt Scale-Sensitive Intersection over Union(SIoU) loss instead of Complete Intersection over Union(CIoU) as a positioning loss function.Experimental results showed that compared with YOLOv5, TCS-YOLO’s model complexity(GFLOPs, Giga Floating Point of Operations) was reduced by an average of 3.13%. Furthermore, the number of parameters was reduced by an average of 0.88% and inference speed was reduced by an average of 0.2 ms, while mean average precision(mAP0.5) increased by 0. 2% on average, and mAP0.5:0.95increased by 1.26% on average. The proposed TCS-YOLO model was compared with the conventional YOLOv3, YOLOv4, YOLOv5, and Swin Transformer models, and TCS-YOLO exhibited more efficient characteristics. The TCS-YOLO model has universal feasibility for the target identification of global oil storage tanks. In combination with techniques to calculate the storage rates of identified oil tanks, this method can provide a technical reference for remote sensing data in the field of energy futures.