Fluid inclusions have important guiding significance for oil and gas resource evaluation, reservoir geochemistry, fluid types, fluid sources, and exploration. However, The identification of fluid inclusions primarily relies on manual searching, a process that is time-consuming and labor-intensive. To address this issue, an improved YOLOv5s fluid inclusion object detection algorithm is proposed in this paper. The feature extraction and feature fusion components of the original YOLOv5s model are enhanced to improve the model's detection capability, making it more suitable for fluid inclusion detection. Acoordinate attention mechanism is introduced in the feature extraction component to enhance localization and recognition capabilities. Additionally, the original path aggregation network in the feature fusion component is replaced with a bidirectional feature pyramid network. The upgraded network possesses stronger feature fusion capabilities, thereby enhancing the detection capability of small targets. Experimental results demonstrate that compared to the original YOLOv5s model, the average precision of the improved YOLOv5s increases from 75.3% to 77.3%, representing a 2 percentage point improvement over the original algorithm. The detection speed also improves from 58.14fps to 62.89fps, resulting in a 4.75fps improvement, thus achieving more accurate and efficient fluid inclusion detection.