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 （mAP_0.5） increased by 0.2% on average， and mAP_0.5：0.95 increased 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.