摘要
随着咖啡文化的普及和消费需求的增长,咖啡果实的成熟度成为决定品质和市场价值的关键因素。然而,不合理采收导致品质参差不齐,影响经济效益。通过先进成熟度检测技术,可提升采摘精准度,为农户提供数据化决策支持,但在复杂背景下现有方法的鲁棒性和高密度小目标检测方面仍存在技术挑战。因此,提出一种基于图像分块交互的咖啡树果实成熟度预测模型,通过引入空间分块交互注意力机制(SBIAM)实现局部特征和全局特征信息的互补融合,使得模型既能聚焦果实区域,又能有效抑制背景干扰,增强模型对关键特征的关注能力。此外,引入归一化Wasserstein距离(NWD)损失函数解决咖啡果实分类较多出现预测位置偏差等问题,提升复杂场景下咖啡果实成熟度检测的精度和鲁棒性。实验结果表明,改进模型不仅提升了检测精度,还实现了性能与效率的良好平衡。
With the popularity of coffee culture and growing consumer demand,the maturity of coffee fruits has become a key determinant of quality and market value.However,irrational harvesting leads to uneven quality and impacts economic benefits.Through advanced ripeness detection techniques,harvesting accuracy can be improved to provide data-based decision support for farmers;however,the existing methods still have technical challenges in terms of robustness in complex backgrounds and high-density small-target detection.Therefore,a coffee tree fruit ripeness prediction model based on image-chunking interaction was proposed,which achieved the complementary fusion of local and global feature information by introducing a spatial-blocking interaction attention mechanism(SBIAM),so that the model can focus on the fruit region as well as effectively inhibit the background interference,enhancing the model's ability to pay attention to key features.In addition,a normalized Wasserstein distance(NWD)loss function was introduced to solve the problems such as the prediction-position deviation common in coffee-fruit classification,thereby improving the accuracy and robustness of coffee-fruit ripeness detection in complex scenes.Experimental results demonstrated that the proposed improved model not only enhanced the detection accuracy,but also achieved a good balance between performance and efficiency.
作者
张馨匀
张力文
周李
罗笑南
ZHANG Xinyun;ZHANG Liwen;ZHOU Li;LUO Xiaonan(Institute of Artificial Intelligence Cross Research,Guilin University of Electronic Science and Technology,Guilin Guangxi 541004,China)
出处
《图学学报》
北大核心
2025年第6期1274-1280,共7页
Journal of Graphics
基金
广西科技重大专项(桂科AA24263013)。
