A Study of Engine Room Smoke Detection Based on Proactive Machine Vision Model for Intelligent Ship

Fire disaster causes damage to ships, pollute the environment, and threatens people's lives, so making early detection and reasonable decision is essential for avoiding catastrophic accidents. Smoke, as the main feature of fire, plays a significant role in early fire identification. However, challenged by the complex engine room (E/R) environment and shooting issues, the recognition performances of current smoke detection methods based on machine vision are unsatisfactory at the early fire stage. To address the issues, this paper proposes a proactive machine vision model based on the fusion of the transfer learning method and proactive perception technology for smoke detection. Firstly, a smoke images database is established based on similar environments (indoor and ship fire scenes) for the transfer learning module training, where more smoke features are extracted and learned from complex scenes. Afterward, real-time images are input into the trained model for local significance analysis which is applied as a triggering criterion for active smoke perception. When the significance indicator reaches the trigger condition, the proactive perception technology adopts reinforcement learning and proactive vision for further identifying detailed information about scenarios. By this processing, the internal and external parameters of the camera are adjusted to narrow and focus the targets of interest in the current scene. Finally, the ship E/R workshop and distribution box fire cases are selected to validate the proposed method. The experiment results indicate that the proposed model outperforms the existing techniques in accuracy as it has the potential to detect earlier smoke features.