This work introduces ASFF, a feature fusion strategy that learns to filter conflicting information across different spatial scales to improve detection accuracy. The implementation modifies YOLOv3’s architecture with an attention-based fusion mechanism and supports multiple backbone options including MobileNetV2. It achieves improved mAP on COCO benchmarks while maintaining reasonable inference speeds compared to baseline YOLOv3.