The accurate identification and localization of tilted droplets is a key preprocessing link for achieving a high-precision measurement of the dynamic contact angle. For the problems of low detection accuracy of traditional algorithms and excessive hardware occupancy of deep learning-based target detection algorithms, this paper proposes Light-YOLOv8OBB, a lightweight tilted droplet detection and localization model based on the improved YOLOv8 algorithm. First, this paper designs a C2f-light convolutional structure to lighten and improve the backbone network. Second, the Slim-Neck design paradigm is introduced into the neck network to further lighten the network model. The convolutional attention mechanism module is added to strengthen the model's ability to detect small target objects. Experiments on a homemade droplet dataset and analysis of the results reveal that our algorithm can balance model performance and detection efficiency well. The mean average precision (mAP@0.5:0.95) value of proposed algorithm reaches 76.7%, an improvement of 7.5 percentage points compared with the base model, whereas the number of parameters and computation decrease by 38.7% and 34.9%, respectively, compared with the base model, and the inference time is only 16.1 ms on NVIDIA GeForce MX250.