Abstract: The accurate segmentation of watermelon fruit in UAV（unmanned aerial vehicle） image is the premise of watermelon counting and yield estimation. This paper proposed a segmentation model of watermelon fruit based on an improved U-Net network to address the problems of false segmentation and inaccurate detail edge segmentation of UAV watermelon images due to complex field background, uneven illumination, and insignificant features. The visible light image of the UAV in the early ripening stage of watermelon was collected to construct the semantic segmentation dataset of watermelon fruit. An efficient channel attention mechanism was introduced in the downsampling process to enhance the feature weight of the fruit region, and a dual attention mechanism was added in the skip connection part to establish rich context dependency based on local features, so as to improve the feature extraction ability of the target region. Then, the feature map and class activation map were used to visually explain the prediction process of the model. Experimental results showed that the Accuracy, Precision, Recall, F1-Score and Intersection over Union（IoU） of the model were 99.03%, 92.67%, 90.55%, 91.21% and 84.71%, respectively, and the processing time of an individual image was 0.145 s. This model can effectively capture the fruit features in the UAV watermelon image in the early maturity stage, accurately identify the fruit regions with complex background under natural environment, and has good segmentation effect and generalization ability. It can provide theoretical basis and technical support for the use of UAV remote sensing technology to count the number of watermelon in the field and estimate the yield at the early maturity stage.