Abstract
International shipping industry undertaking more than 80% global trade volume, is indispensable to the world’s economy and development. Seafarers, which are an important part of shipping, mainly engage in safe navigation and good maintenance of ships. However, they are prone to expose themselves to occupational accidents and injuries, especially fatal trauma on the head due to non-helmet-use behaviour. Wearing a safety helmet can effectively reduce the risks resulting in head injuries. Despite the vital role of safety helmet, many seafarers tend to take off their helmet because of discomfort caused by weight and high temperature in engine room or deck. To improve the ship’s safe operation and prevent the loss of life at sea, this paper proposed a state-of-the-art method based on YOLOv3 model for automatic and real-time detection of non-helmet-use behaviour. The experimental results showed that the detection method can run real-time under ship’s video surveillance and precisely detect the non-helmet-use behaviour with low miss rate.
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