论文标题
评估玉米青贮饲料中核片段识别的模型选择
Evaluation of Model Selection for Kernel Fragment Recognition in Corn Silage
论文作者
论文摘要
在为特定用例设计深度学习系统时，模型选择可能是一项具有挑战性的任务，因为存在许多选项，并且很难知道它们之间的权衡。因此，我们研究了许多最有用的CNN模型，用于测量收获的玉米青贮饲料中核碎片的任务。根据模型复杂性，准确性和速度之间的权衡，对模型进行了许多特征提取器和图像大小的评估，以确定最佳模型设计选择。我们表明，可以通过更复杂的元构造方法进行准确性改进，并且可以通过仅略有精度损失来减小图像大小来优化速度。此外，我们在相交的平均精度上表现出改善，而与先前发表的工作相比，相比的0.5的汇率为0.5的0.5，同时也减少了推理时间。更好的模型选择的结果为创建系统提供了机会，可以帮助农民在收获时改善他们的青贮饲料质量。
Model selection when designing deep learning systems for specific use-cases can be a challenging task as many options exist and it can be difficult to know the trade-off between them. Therefore, we investigate a number of state of the art CNN models for the task of measuring kernel fragmentation in harvested corn silage. The models are evaluated across a number of feature extractors and image sizes in order to determine optimal model design choices based upon the trade-off between model complexity, accuracy and speed. We show that accuracy improvements can be made with more complex meta-architectures and speed can be optimised by decreasing the image size with only slight losses in accuracy. Additionally, we show improvements in Average Precision at an Intersection over Union of 0.5 of up to 20 percentage points while also decreasing inference time in comparison to previously published work. This result for better model selection enables opportunities for creating systems that can aid farmers in improving their silage quality while harvesting.
