A water contact angle measurement method based on the improved Faster RCNN is proposed to address the issues of low accuracy and poor reproducibility caused by manual intervention in traditional water contact angle measurement processes. First, the Faster RCNN backbone network VGG16 was replaced with ResNet101, and the attention mechanism model convolutional block attention module (CBAM) was added at the end of its residual block to enhance the network's ability to extract features. Second, the feature pyramid network (FPN) was incorporated to fully extract feature information at different scales, and the Focal loss function was introduced to solve the problem of imbalanced positive and negative class samples. Finally, edge detection and corner extraction were performed on the located water droplets, and then the iterative reweighted least squares (IRLS) method was used to fit the elliptical contour to calculate the contact angle angle. The experimental results show that the improved Faster RCNN object detection algorithm improves mean average precision by 10.794% and speed by 11 frame/s over the original algorithm. The average standard deviation of contact angle angle measurements is 0.109°.