In the preprocessing stage of fully automatic medical inspection pipelines， a test tube and liquid level detection algorithm based on improved YOLOv8 was proposed to address the issues of low recognition rate and low speed of test tube sizes and liquid levels in traditional detection methods. Firstly， a lightweight ADown module was employed to replace the Conv module in the backbone network for feature extraction and downsampling， thereby extracting more effective information while reducing the model size. Secondly， Bi-directional Feature Pyramid Network （BiFPN） was utilized for feature fusion， and more hierarchical feature information was integrated through bidirectional and skip connections. Additionally， Omni-dimensional Dynamic Convolution （ODConv） was introduced at the neck， and the C2f-ODConv module was designed to enhance feature extraction capabilities. Finally， the Inner-CIoU bounding box loss function was introduced to accelerate model convergence by auxiliary bounding boxes. Experimental results demonstrate that the proposed algorithm achieves improvements of 3.6， 4.8 and 5.0 percentage points in precision， recall and mean Average Precision （mAP）@50， and a decrease in computational cost （FLOPs） by 13.6% on a self-made dataset. It can be seen that the proposed model can realize accurate recognition of test tubes and liquid levels in real scenarios.