JB
J.R. Buitenweg
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1
The Sim2Real gap poses significant challenges for testing autonomous vehicles, often becoming apparent only during high-risk real-world deployments. This research proposes a novel pipeline that leverages both high-fidelity (CARLA) and low-fidelity (Gym-Duckietown) simulators to estimate this gap prior to deployment. The results reveal a strong corelation between performance in Gym Duckietown and real-world outcomes, suggesting it can serve as potential estimation for real world performance and the Sim2Real gap. Nonetheless, real-world testing remains an essential part of the validation process. Future work should build on these findings to further explore and validate the approach.
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The Sim2Real gap poses significant challenges for testing autonomous vehicles, often becoming apparent only during high-risk real-world deployments. This research proposes a novel pipeline that leverages both high-fidelity (CARLA) and low-fidelity (Gym-Duckietown) simulators to estimate this gap prior to deployment. The results reveal a strong corelation between performance in Gym Duckietown and real-world outcomes, suggesting it can serve as potential estimation for real world performance and the Sim2Real gap. Nonetheless, real-world testing remains an essential part of the validation process. Future work should build on these findings to further explore and validate the approach.
To reduce food waste, the strawberry harvesting process should be optimized. In the modern era, computer vision can provide huge amounts of help. This paper focuses on optimizing pre-trained convolutional neural networks (CNN) to determine the maturity level of strawberries on a 1-10 scale. Here, 1 means unripe and 10 means overripe. Maturity level 8 is marketable. Experiments are done with VGG19, Resnet50, InceptionV2, Alexnet, and EfficientNetB2 as classifiers on segments using ADAM and SGD as optimizers and cross-entropy as loss function. The same CNN's are applied as a backbone for FasterRCNN to see how they would behave within an object detection architecture. The biggest challenge during this research was the low amount of training data. The research showed that using convolutional neural networks as a maturity level predictor is possible, but a well made training set with an equal spread for each maturity level is necessary to possibly achieve high accuracy.
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To reduce food waste, the strawberry harvesting process should be optimized. In the modern era, computer vision can provide huge amounts of help. This paper focuses on optimizing pre-trained convolutional neural networks (CNN) to determine the maturity level of strawberries on a 1-10 scale. Here, 1 means unripe and 10 means overripe. Maturity level 8 is marketable. Experiments are done with VGG19, Resnet50, InceptionV2, Alexnet, and EfficientNetB2 as classifiers on segments using ADAM and SGD as optimizers and cross-entropy as loss function. The same CNN's are applied as a backbone for FasterRCNN to see how they would behave within an object detection architecture. The biggest challenge during this research was the low amount of training data. The research showed that using convolutional neural networks as a maturity level predictor is possible, but a well made training set with an equal spread for each maturity level is necessary to possibly achieve high accuracy.