Although existing object detection models have achieved remarkable results in processing speed and accuracy, their performance often deteriorates when dealing with small targets. To address this issue, we propose Feature Fusion YOLO (FFYOLO) based on YOLOv8n. Specifically, we utilize a lightweight CNN based Cross-scale Feature Fusion module (CCFM) to integrate features of different scales. Simultaneously, an adaptive spatial feature fusion (ASFF) method is introduced to effectively filter out conflicting information and enhance scale invariance. Finally, an additional detection head is added to further improve the detection capability for small targets. Experiments conducted on the VisDrone-DET2019 dataset demonstrate that FFYOLO achieves an mAP@0.5 of 40.2%, representing an 8% increase compared to the baseline YOLOv8n model.