Decommissioning of the Fukushima Daiichi nuclear power station is challenging due to industrial and chemical hazards as well as radiological ones. The decommissioning workers in these sites are indicated to wear Personal Protective Equipment (PPE) appropriately for radiation protection. In response to the difficulties of on-site PPE management in decommissioning site of Fukushima Daiichi NPS, this paper proposes the combination of deep learning-based object detection and individual detection using geometry relationships analysis to automatically facilitate the safety monitoring task of decommissioning workers to ensure hardhats, full-face masks and dust masks are correctly used for each zone. The experimental results demonstrate that the high precision, high recall and fast speed of the proposed approach can effectively detect decommissioning workers’ incorrect use of PPE.

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