Object detection method for ship safety plans using deep learning

Abstract

During the safety inspection of a ship, there is a stage confirming whether the safety plan is designed in accordance with the regulations. In this process, an inspector checks whether the location and number of various objects (safety equipment, signs, etc.) included in the safety plan meet the regulations. Manually converting the information of objects existing in the ship safety plan into digital data requires significant effort and time. To overcome this problem, a technique is required for automatically extracting the location and information of the object in the plan. However, owing to the characteristics of the ship safety plan, there are frequent cases in which the detection target overlaps with noise (figure, text, etc.), which lowers the detection accuracy. In this study, an object detection method that can effectively extract the object quantity and location within the ship safety plan was proposed. Among various deep learning models, suitable models for object detection in ship safety plans were compared and analyzed. In addition, an algorithm to generate the data necessary for training the object detection model was proposed and adopted the feature parameters, which showed the best performance. Subsequently, a specialized object detection method to rapidly process a large ship safety plan was proposed. The method proposed in this study was applied to 15 ship safety plans. Consequently, an average recall of 0.85 was achieved, confirming the effectiveness of the proposed method.