In recent years

the object detection in remote sensing imagery has been a hot research spot with the development of remote sensing technique. To deal with the complex background in the imagery and the detection model’s interpretability

we propose a weakly semantic based attention network for interpretable object detection model in remote sensing imagery. Firstly

a feature pyramid network is devised for the variation of object scales. Next

an angle is added to the regression to better locate the object. Thirdly

we add a weakly semantic segmentation network to enhance feature and filter the noisy information in the background. Finally

the model is dissected by the proposed method to get the interpretable semantic concepts of convolutional kernel. Experiments validated that the model has a good performance in the aspect of suppressing the background noise and make our model easy to comprehend.