山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (2): 89-98.doi: 10.6040/j.issn.1672-3961.0.2021.300
王心哲1,邓棋文1,王际潮2,范剑超3*
WANG Xinzhe1, DENG Qiwen1, WANG Jichao2, FAN Jianchao3*
摘要： 采用无监督方法与深度学习模型结合,解决筏式养殖边缘信息精确提取问题,提出深度语义分割(semantic segmentation, SegNet)-马尔科夫随机场(Markov random field, MRF)模型,该模型提取目标空间细节信息和深度判别特征信息。通过SegNet编码器的卷积和最大池化提取筏式养殖的特征信息和扩大感受野,抑制噪声、误判等现象的产生,模型后端接入MRF模型,计算像素空间领域内的特征信息进行聚类分析来获取目标低水平的空间细节信息,在深度特征信息的基础上较大程度的保留空间特征信息,完善边缘信息并抑制连通区域的产生。试验结果表明,该模型极大减少了特征信息丢失和因海水背景而产生的误判,其分类精度高于95%,明显优于经典无监督算法和单一的深度学习模型。
中图分类号:
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