| Citation: | ZHANG D D，WANG C P，FU Q. Ship’s critical part detection algorithm based on anchor-free in optical remote sensing[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（4）：1365-1374 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0450
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Low detection effectiveness and inadequate refinement plague the existing deep learning-based remote sensing ship detection technique. To address the above problems, an optical remote sensing ship critical part detection algorithm based on anchor-free is proposed. The proposed algorithm takes fully convolutional one-stage object detection (FCOS) as the benchmark algorithm and introduces a global context module in the backbone network to improve the feature representation capability of the network. In the prediction step, a regression branch with orientation representation capabilities is built to more accurately describe the orientation of targets. The centrality function is optimized to make it direction-aware and adaptive. The experimental results show that the average precision (AP) of the proposed algorithm is significant improved over FCOS algorithm on the self-built ship critical part dataset and HRSC2016, respectively. Compared with other algorithms, the proposed algorithm has superior performance in both detection speed and detection accuracy and has high detection efficiency.
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