文章基本信息

标题：Auxiliary Equipment Detection in Marine Engine Rooms Based on Deep Learning Model
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作者：Jiahao Qi ; Jundong Zhang ; Qingyan Meng 等
期刊名称：Journal of Marine Science and Engineering
电子版ISSN：2077-1312
出版年度：2021
卷号：9
期号：9
页码：1006
DOI：10.3390/jmse9091006
语种：English
出版社：MDPI AG
摘要：In the intelligent perception of the marine engine room, visual identification of auxiliary equipment is the prerequisite for defect recognition and anomaly detection. To improve the detection accuracy, this study presents an auxiliary equipment detector in the cabin based on a deep learning model. Owing to the compact layout of pipeline networks and the large disparity in the equipment scales, we initially adopted RetinaNet as the basic framework, and introduced the single channel plain architecture RepVGG as the feature extraction network to simplify the complexity and improve realtime detection. Secondly, the Neighbor Erasing and Transferring Mechanism (NETM) was applied in the feature pyramid to deal with more complicated scale variations. Then, the complete IoU (CIoU) regression loss function was used instead of smooth L1, and the DIoU Soft-NMS mechanism was proposed to alleviate the misdetection in congested cabins. Further, comparison experiments and ablation experiments were performed on the auxiliary equipment in a marine engine room (AEMER) dataset to validate the efficacy of these strategies on the model performance boost. Specifically, our model can correctly detect 93.44% of coolers, 100.00% of diesel engines, 60.26% of meters, 95.30% of pumps, 55.01% of reservoirs, 97.68% of oil separators, and 74.37% of valves in a practical cabin.