50 citations · 62 across the 6 of their papers we have counts for
5 papers · 1 filter
A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection
Pedro Conde, Rui L. Lopes, Cristiano Premebida
The proliferation of Deep Neural Networks has resulted in machine learning systems becoming increasingly more present in various real-world applications. Consequently, there is a g…
Approaching Test Time Augmentation in the Context of Uncertainty Calibration for Deep Neural Networks
Pedro Conde, Tiago Barros, Rui L. Lopes +2
With the rise of Deep Neural Networks, machine learning systems are nowadays ubiquitous in a number of real-world applications, which bears the need for highly reliable models. Thi…
ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in Orchards
T. Barros, L. Garrote, P. Conde +4
Robust and reliable place recognition and loop closure detection in agricultural environments is still an open problem. In particular, orchards are a difficult case study due to st…
Probabilistic Approach for Road-Users Detection
G. Melotti, W. Lu, P. Conde +4
Object detection in autonomous driving applications implies that the detection and tracking of semantic objects are commonly native to urban driving environments, as pedestrians an…
Multispectral Vineyard Segmentation: A Deep Learning approach
T. Barros, P. Conde, G. Gonçalves +4
Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural…