The number of maize seedlings is the essential information for sowing quality assessment. It is important to obtain the number of maize seedlings quickly and precisely for investigation and filling the gaps with seedlings. To improve the real time and precision of the acquisition of maize seedling number, the YOLO model (FE-YOLO) was improved, and the detection and acquisition of maize seedling number were realized. Firstly, dynamic ReLU was used to improve the bottleneck layer of MobileNet and the feature extraction performance of MobileNet was increased. Then, according to the target size and spatial texture characteristics of maize seedlings, the multi-receptive field fusion and spatial attention mechanism were used to enhance the feature expression. The experimental results showed that the FE-YOLO model enhanced the spatial texture characteristics of the seedlings, reduced the complexity of the model, made the mAP and recall rates reach 87.22% and 91.54%, respectively, and the floating-point operations per second and detection consumption time were only 7.91% and 33.76% of YOLO v3. FE-YOLO can detect the maize seedlings in the UAV orthoimage, and then Equation (13) was used to estimate the planting density. FE-YOLO had low complexity and high recognition accuracy, which can provide support for maize seedling management.