Research Article
Marine Vessel Detection in Sea Fog Environment Based on SSD
@INPROCEEDINGS{10.1007/978-3-031-34899-0_4, author={Yuanyuan Wang and Ning Wang and Luyuan Tang and Wei Wu}, title={Marine Vessel Detection in Sea Fog Environment Based on SSD}, proceedings={Sensor Systems and Software. 13th EAI International Conference, S-Cube 2022, Dalian, China, December 7-9, 2022, Proceedings}, proceedings_a={S-CUBE}, year={2023}, month={6}, keywords={Deep learning Marine vessel detection Fog image object detection}, doi={10.1007/978-3-031-34899-0_4} }- Yuanyuan Wang
Ning Wang
Luyuan Tang
Wei Wu
Year: 2023
Marine Vessel Detection in Sea Fog Environment Based on SSD
S-CUBE
Springer
DOI: 10.1007/978-3-031-34899-0_4
Abstract
Aiming at solving the problem of low marine vessel detection accuracy in the sea fog environment, a deep learning-based anti-fog marine vessel detection method is proposed in this paper by combining defogging preprocessing with marine vessel detection model. Firstly, gated context aggregation network (GCANet) network is used to process the marine vessel image. Then, the processed image is sent to a modified SSD network, wherein anchors are tuned by statistical characteristics of the shape of marine vessel to detect the position of the marine vessel. Furthermore, to alleviate the loss of feature information due to defogging processing, channel attention mechanism based on the squeeze and excitation module (SE) is added to base convolutional layer of SSD. The comprehensive experiments and comparison results show that the proposed G-SEMSSD network is more suitable for marine vessel detection under sea fog environment.
