In response to the challenges encountered in the detection of oil leakage from pipelines on offshore platforms, such as dust and fog interference, varying target scales, and complex backgrounds, a detection method for oil leakage from offshore platform pipelines based on an improved YOLOv8 model is herein proposed.Initially, the C2f_MP module is employed to substitute the C2f module within the backbone network. This substitution effectively enhances the model′s capacity to extract detailed features. Subsequently, an efficient multi-scale attention mechanism (EMA) is incorporated into the model′s neck structure. This addition significantly improves the model′s focus on features of multi-scale targets within complex scenarios, thereby enhancing its ability to recognize small targets.Finally, the original detection head is optimized into four lightweight small target detection heads. This optimization remarkably improves the detection performance for small targets. Moreover, the WIoU loss function is utilized to boost the training effectiveness and enhance the model′s recognition accuracy.The experimental results indicate that the improved YOLOv8 model can maintain a real-time detection speed of 118 fps. Simultaneously, compared with the baseline model YOLOv8s, the precision is increased by 2.3%, and the mAP@0.5 is enhanced by 2.4%. The practical application tests demonstrate that the average accuracy of the improved model in detecting oil leakage from pipelines on a particular offshore platform reaches 96.25%. This achievement meets the requirements of engineering applications and offers an effective technical solution for the safety monitoring of offshore platform pipelines under complex industrial environment backgrounds.