Single Shot Feature Aggregation Network for Underwater Object Detection | |
Zhang, Lu3,5; Yang, Xu3; Liu, Zhiyong3,5,6; Qi, Lu4; Zhou, Hao1; Chiu, Charles2 | |
2018-08 | |
会议日期 | 2018-8 |
会议地点 | Beijing, China |
英文摘要 | The rapidly developing ocean exploration and observation make the demand for underwater object detection become increasingly urgent. Recently, deep convolutional neural networks (CNN) have shown strong ability in feature representation and CNN-based detectors also achieve remarkable performance, but still facing the big challenge when detecting multi-scale objects in a complex underwater environment. To address this challenge, we propose a novel underwater object detector, introducing multiscale features and complementary context information for better classification and location ability. In the auto-grabbing contest of 2017 Underwater Robot Picking Contest sponsored by National Natural Science Foundation of China (NSFC), we won the 1-st place by using proposed method for real coastal underwater object detection. |
会议录出版者 | IEEE Xplore |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/44935] |
专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
通讯作者 | Liu, Zhiyong |
作者单位 | 1.Harbin Engineering University Harbin 2.School for Higher and Professional Education 3.State Key Laboratory of Management and Control for Complex Chinese Academy of Sciences Systems, Institute of Automation 4.The Chinese University of Hong Kong 5.University of Chinese Academy of Sciences 6.Center for Excellence in Brain Science and Intelligence Technology |
推荐引用方式 GB/T 7714 | Zhang, Lu,Yang, Xu,Liu, Zhiyong,et al. Single Shot Feature Aggregation Network for Underwater Object Detection[C]. 见:. Beijing, China. 2018-8. |
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