2,400 research outputs found
Evaluation of Model Selection for Kernel Fragment Recognition in Corn Silage
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.Comment: Paper presented at the ICLR 2020 Workshop on Computer Vision for
Agriculture (CV4A
The Challenge of Data Annotation in Deep Learning – A Case Study on Whole Plant Corn Silage
Recent advances in computer vision are primarily driven by the usage of deep learning, which is known to require large amounts of data, and creating datasets for this purpose is not a trivial task. Larger benchmark datasets often have detailed processes with multiple stages and users with different roles during annotation. However, this can be difficult to implement in smaller projects where resources can be limited. Therefore, in this work we present our processes for creating an image dataset for kernel fragmentation and stover overlengths in Whole Plant Corn Silage. This includes the guidelines for annotating object instances in respective classes and statistics of gathered annotations. Given the challenging image conditions, where objects are present in large amounts of occlusion and clutter, the datasets appear appropriate for training models. However, we experience annotator inconsistency, which can hamper evaluation. Based on this we argue the importance of having an evaluation form independent of the manual annotation where we evaluate our models with physically based sieving metrics. Additionally, instead of the traditional time-consuming manual annotation approach, we evaluate Semi-Supervised Learning as an alternative, showing competitive results while requiring fewer annotations. Specifically, given a relatively large supervised set of around 1400 images we can improve the Average Precision by a number of percentage points. Additionally, we show a significantly large improvement when using an extremely small set of just over 100 images, with over 3Ă— in Average Precision and up to 20 percentage points when estimating the quality
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