LF-YOLOv7: Improved YOLOv7 Based on Lightweight Modules and Novel Feature Fusion for Object Detection on Drone-Captured Scenarios

To address challenges in drone-captured images, including small-sized objects, multiple scales, and object diversity, we propose a novel lightweight and feature fusion based object detection model, called LF-YOLOv7. Firstly, a comprehensive high-resolution refinement design is investigated through simultaneously restructuring the head and optimizing the backbone, achieving more accurate assignment of multiple anchor boxes of different sizes and higher spatial resolution with lightweight mode. Furthermore, we present a new feature fusion module SM-BiFPN, which effectively integrates shallow details through cross-layer connections and enables the model to obtain more feature information related to small objects. Experimental evaluation shows that our method achieves a more competitive results than the benchmark and other compared state-of-the-art models in terms of mAP50 and the number of parameters.