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- W4308659927 abstract "With the ever-increasing demand for marine products with high nutritional value in the sea, rapid and accurate detection of target organisms is highly desired. In this context, a novel underwater object detection algorithm based on improved Single Shot MultiBox Detector (SSD) is proposed to improve the detection accuracy and real-time performance in complex underwater environment. The proposed algorithm employs ResNeXt-50 instead of VGGNet-16 as the backbone network to improve the ability to obtain details and position information of the target object. To be more specific, a channel-spatial attention mechanism is incorporated in the deep layers of the improved XC-SSD model to enhance semantic information of high-level feature maps and reduce missed detection rate and false detection rate. Comprehensive experiments on the URPC-2019 dataset show that the detection accuracy and speed of the proposed algorithm are 79.76% and 18.95 FPS, respectively, which are 3.49% and 2.8 FPS higher than the original SSD algorithm. Experimental results clearly demonstrate that the proposed algorithm is highly efficient and suitable for real-time object detection." @default.
- W4308659927 created "2022-11-13" @default.
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- W4308659927 date "2022-09-23" @default.
- W4308659927 modified "2023-09-25" @default.
- W4308659927 title "Underwater Object Detection Based on Improved SSD with Convolutional Block Attention" @default.
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- W4308659927 doi "https://doi.org/10.1109/icoias56028.2022.9931319" @default.
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