Unmanned aerial vehicle (UAV) aerial image object detection is a valuable and challenging research field. Despite the breakthrough of deep learning-based object detection networks in natural scenes, UAV images often exhibit characteristics such as a high proportion of small objects, dense distribution, and significant variations in object scales, posing great challenges for accurate detection. To address these issues, we propose an innovative multiscale feature fusion small object detection network (MFFSODNet). First, concerning the high proportion of small objects in UAV images, an additional tiny object prediction head is introduced instead of the large object prediction head. This approach provides a good detection accuracy of small objects and significantly reduces the parameters. Second, to enhance the feature extraction capability of the network for fine-grained information from small objects, a multiscale feature extraction module (MSFEM) is designed, which could extract rich and valuable multiscale feature information through convolution operation of different scales on multiple branches. Third, to fuse the fine-grained information from shallow feature maps and the semantic information from deep feature maps, a new bidirectional dense feature pyramid network (BDFPN) is proposed. By expanding the feature pyramid network scale and introducing skip connections, BDFPN achieves efficient multiscale information fusion. Extensive experiments on the VisDrone and UAVDT benchmark datasets demonstrate that MFFSODNet outperforms the state-of-the-art object detection methods and further validate the effectiveness and generalization of MFFSODNet on photovoltaic array defect datasets (PVDs).