| 党相昭,何赟泽,程 亮,杜 闯,刘圳康,杨春利,王磊刚,杨士远.面向水域场景的热成像人员识别算法研究[J].电子测量与仪器学报,2022,36(8):187-193 |
| 面向水域场景的热成像人员识别算法研究 |
| Research on thermal imaging personnel recognition algorithm for water scene |
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| DOI： |
| 中文关键词: 红外热成像 目标检测 YOLO 智能救生 无人艇 |
| 英文关键词:infrared thermal imaging object detection YOLO intelligent lifesaving unmaned surface vessel |
| 基金项目:湖南省自然科学基金重大项目（S2021JJZDXM0022）、湖南省重点研发计划（S2021GCZDYF0800）、珠海云洲智能科技有限公司委托课题（H202191400326）项目资助 |
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| Author | Institution |
| Dang Xiangzhao | 1. College of Electrical and Information Engineering, Hunan University |
| He Yunze | 1. College of Electrical and Information Engineering, Hunan University |
| Cheng Liang | 2. School of Ocean Engineering, Jiangsu Ocean University,3. Zhuhai Yunzhou Intelligent Technology Co. , Ltd. |
| Du Chuang | 1. College of Electrical and Information Engineering, Hunan University |
| Liu Zhenkang | 1. College of Electrical and Information Engineering, Hunan University |
| Yang Chunli | 3. Zhuhai Yunzhou Intelligent Technology Co. , Ltd. |
| Wang Leigang | 3. Zhuhai Yunzhou Intelligent Technology Co. , Ltd. |
| Yang Shiyuan | 3. Zhuhai Yunzhou Intelligent Technology Co. , Ltd. |
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| 中文摘要: |
| 针对水域场景夜间能见度极低,难以实现人员目标检测与定位的问题,结合红外热成像技术与深度学习目标检测算法,
研究了一种黑暗环境下水域人员目标检测方法。 经过多场景实地采集,自主构建了一套热成像水域场景下的人员目标数据集
IR-YZ。 在对比经典目标检测方法在 IR-YZ 数据集上的性能的基础上,针对热成像特点与水域环境特点,提出了一种增强型轻
量级水上目标检测网络 IWPT-YOLO(infrared water person target-YOLO)。 实验结果表明,IWPT-YOLO 算法具有精确、快速、简洁
等优势,其模型大小为 93 MB,平均精度 mAP 达到了 85. 34%,检测速度达到了 20. 975 FPS,比经典算法 YOLOv3 网络与 SSD 网
络在模型大小、平均精度与检测速度上均有提高,验证了 IWPT-YOLO 算法对水域场景下的热成像人员目标具有更好的检测性
能,更明显的优势。 |
| 英文摘要: |
| Aiming at the problem of the extremely low visibility of water scene low at night, which results in the difficulty in detecting
and locating personnel targets, the author combines infrared thermal imaging technology with deep learning object detection algorithm to
study an object detection method for people in dark water area. After multi-scene field collection, a set of human target data set IR-YZ in
thermal imaging water scene was independently constructed. On the basis of the performance of the IR-YZ data set and compared with the
classical object detection methods, environmental characteristics, an enhanced lightweight water object detection network infrared water
person target-YOLO is proposed, featuring the characteristics of thermal imaging and water areas. The experimental results show that the
IWPT-YOLO algorithm has the advantages of being more accurate, faster and more concise than those of the classical algorithm. The
model size is 93 MB, the average precision mAP reaches 85. 34%, and the detection speed reaches 20. 975 FPS. Compared with the
classic algorithm YOLOv3 network and SSD network, the model size, average precision and detection speed are all improved. It verifies
that the IWPT-YOLO algorithm has better detection performance and more obvious advantages for the characteristics of thermal imaging
and water areas. |
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