The existing regional intrusion detection methods for oilfield operation are exposed to problems of poor detection efficiency and failure in real-time detection due to the long distance and small targets. Therefore, this study proposes an offshore intrusion detection model to improve the safety supervision efficiency and ensure the accuracy and timeliness of intrusion detection in the offshore perimeter of oilfield operation sites. This model is based on the combination of SOLOv2 and CenterNet. First, the model uses SOLOv2 to segment the perimeter of the offshore area and identifies the danger area from these segmentation results. Then, CenterNet is adopted to detect and locate the operator and calculate the location of the operator and the danger area to realize the intrusion detection in the perimeter of the offshore area. The experimental results prove that this method can effectively solve the intrusion detection problems in offshore perimeter areas including complex backgrounds, high detection accuracy requirements, and small targets, and the accuracy of the model can reach 94.7%. This model has been successfully implemented in the oilfield operation sites with good performance.