Abstract
In many scenarios, such as power station, the detection of whether wearing safety helmets or not for perambulatory workers is very essential for the safety issue. So far, research in safety helmets wearing detection mainly focused on hand-crafted features, such as color or shape. With rising success of deep learning, accurately detecting objects by training the deep convolutional neural network (DCNN) becomes a very effective way. This paper presents a deep learning approach for accurate safety helmets wearing detection in employing a single shot multi-box detector (SSD). Moreover, because of safety helmet usually relatively small and unfortunately SSD struggles in detecting very small objects, a novel and practical safety helmet wearing detecting system is proposed, Finally, extensive compelling experimental results in power substation illustrate the efficiency and effectiveness of our work.
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