The variable scales of objects and the use of feature fusion have been the challenges for popular object detection algorithms. Considering the problems, this study proposes a multi-path feature fusion module, which strengthens the connection between input and output features and alleviates the dilution of feature information in transmission by adopting cross-scale and cross-path feature fusion. Meanwhile, the study also proposes a scale-aware module by refining the attention model, which allows the model to easily recognize multi-scale objects by selecting the size of the receptive field corresponding to the scale of the objects independently. After the scale-aware module is embedded into the multi-path feature fusion module, the feature extraction and utilization abilities of the model are improved. The experimental results reveal that the proposed method achieves 82.2 mAP and 38.0 AP on PASCAL VOC and MS COCO datasets, respectively, an improvement of 1.3 mAP and 0.6 AP over the baseline FPN Faster RCNN, respectively, with the most significant improvement in detection of small-scale objects.