为抑制船舶图像目标检测受光照变化、海浪干扰、背景杂波等因素的影响，设计视觉显著性和稀疏表示学习相融合的船舶图像目标检测方法，提升船舶图像目标检测效果。利用船舶图像建立船舶图像字典；通过稀疏表示算法结合字典，稀疏编码船舶图像；依据稀疏编码结果，在船舶图像内提取视觉显著图；通过自适应阈值法，分割视觉显著图，得到船舶目标候选区域，缩小船舶目标检测范围；在概率神经网络内，输入船舶目标候选区域，判断其是否为船舶目标，完成船舶图像目标检测。实验证明，该方法可有效稀疏编码船舶图像，并提取视觉显著图；该方法可有效分割视觉显著图；在简单背景与复杂背景下，该方法均可精准检测船舶目标。
In order to suppress the influence of light change, wave interference, background clutter and other factors in ship image target detection, a ship image target detection method combining visual salience and sparse representation learning is studied to improve the ship image target detection effect. Using ship image to build ship image dictionary; The ship image is sparsely encoded by sparse representation algorithm combined with dictionary. Based on sparse coding results, visual significance maps are extracted from ship images. Through the adaptive threshold method, the visual significance map is segmented to obtain the candidate region of ship target and narrow the detection range of ship target. In the probabilistic neural network, the candidate region of the ship target is input to judge whether it is the ship target, and the ship image target detection is completed. Experimental results show that the proposed method can effectively sparsely encode ship images and extract visual significance images. The method can effectively segment the visual significance map. This method can accurately detect ship targets in both simple and complex backgrounds.
2024,46(8): 157-160 收稿日期：2023-8-22
DOI：10.3404/j.issn.1672-7649.2024.08.029
分类号：TP394.1
基金项目：广西图像图形与智能处理重点实验室（桂林电子科技大学）开放基金项目（GIIP2210）；国家自然科学基金资助项目（62262007）
作者简介：钟思(1982-),女,硕士,高级工程师,研究方向为计算机应用、图形图像及人工智能。
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