In order to accurately identify the tassels of maize at tasseling stage, the growth, plant count and yield of maize should be monitored, based on the CenterNet object detection model without anchor frame, an improved maize tassel recognition model was proposed by analyzing the size distribution of maize tassels and adding position coordinates in the feature extraction network. According to the small tassel size, the feature extraction module for image scale reduction in CenterNet network was removed to reduce the model parameters and improve the detection speed. The location information was added to the CenterNet feature extraction model to improve the positioning accuracy and reduce the rate of tassel missed detection. The experimental results showed that, compared with YOLO v4 and Faster R-CNN with anchor frame, the improved CenterNet model achieved 92.4% accuracy in identifying maize tassels from UAV remote sensing images, which were 26.22 and 3.42 percentage points higher than that of Faster R-CNN and YOLO v4 models, respectively. The detection speed was 36f/s, 32f/s and 23f/s higher than that of the Faster R-CNN and YOLO v4 models, respectively. The method proposed can accurately detect the smaller tassels in the UAV remote sensing image, and provide a reference for the monitoring of agricultural situation in the tasseling stage of maize.