Transmission towers play a crucial role in overhead transmission line systems and are the key target of transmission line inspections. With the help of remote sensing technology

transmission towers can be effectively detected in wide areas at reasonable costs and in a relatively short time period. However

it is difficult to identify the type of transmission towers in optical remote sensing images due to detail degradation caused by long-distance and high-altitude imaging. This paper proposes a transmission tower detection method in optical remote sensing images using an oriented object detector and object and shadow joint detection. To enrich the information

the transmission towers and their shadows are jointly detected through a CenterNet detector with an orientation prediction branch. To improve the detection accuracy of difficult objects

attention and deformable convolutional network modules are introduced to the backbone and orientation prediction branches

respectively. Considering the orientation and the aspect ratio of the objects and shadows

a focal loss function with an aspect ratio is employed to further improve the accuracy. Object and shadow joint detection are separately realized through the one-box and multi-box detection strategies. A transmission tower dataset RSITT labeled with horizontal and oriented boxes is established. Experiments conducted on the RSITT dataset have demonstrated that the detection accuracy and recall rate of the proposed joint detection algorithm reached 73.2% and 95.2%.