The monitoring environment of offshore oil platforms is complex, the monitoring angle of the oil production working platform is different, the marine environment is complex and changeable, and the camera pictures are blurred in the weather such as fog and rain. To solve the above problem of increasing the difficulty of object detection, the object detection algorithm based on Convolutional Neural Network (CNN) in complicated scenario (ODCS) is proposed to detect specific objects in the image. This method integrates feature map prediction with different resolutions to naturally process objects of various sizes, eliminates the feature re-sampling phase, and encapsulates all calculations in a single network. This is easy to train and can be integrated directly into the system that needs to detect components. The experimental results show that compared with the traditional methods, the detection accuracy of this method and the recall rate are significantly improved, and the detection efficiency can meet the requirements of real-time applications.