Aerial scenario detection has always been an interesting challenge due to its various object scales and complex background, making the model hard to cover all the shapes, patterns, and recognize labels for detected objects. In this study, we contribute an extended network based on FasterNet block that can be implemented to the ELAN backbone network of the YOLOv7 architecture without harming the final performance or any adjustment in model width and depth scale while still being able to obtain a lower number in parameter, computation, and faster inference speed. Moreover, the proposed architecture is also combined with a Bi-direction Feature Pyramid Network to enhance model detection capability, allowing the architecture to detect more small objects. As a result, our proposed method reached 73.1% mAP50 on the DIOR test set. The proposed model achieved better than the YOLOv7 baseline model in both performance and complexity.