Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies

Choi, Taeyeong, Would, Owen, Salazar-Gomez, Adrian and Cielniak, Grzegorz (2022) Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies. In: IEEE International Conference on Robotics and Automation, 23-27 May 2022, Philadelphia, USA.

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Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies
Submitted version to ICRA2022.
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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


Data augmentation can be a simple yet powerful tool for autonomous robots to fully utilise available data for self-supervised identification of atypical scenes or objects. State-of-the-art augmentation methods arbitrarily embed structural peculiarity in focal objects on typical images so that classifying these artefacts can provide guidance for learning representations for the detection of anomalous visual inputs. In this paper, however, we argue that learning such structure-sensitive representations can be a suboptimal approach to some classes of anomaly (e.g., unhealthy fruits) which are better recognised by a different type of visual element such as “colour”. We thus propose Channel Randomisation as a novel data augmentation method for restricting neural network models to learn encoding
of “colour irregularity” whilst predicting channel-randomised images to ultimately build reliable fruit-monitoring robots
identifying atypical fruit qualities. Our experiments show that (1) the colour-based alternative can better learn representations for consistently accurate identification of fruit anomalies in various fruit species, and (2) validation accuracy can be monitored for early stopping of training due to positive correlation between the colour-learning task and fruit anomaly detection. Moreover, the proposed approach is evaluated on a new anomaly dataset Riseholme-2021, consisting of 3:5K strawberry images collected from a mobile robot, which we share with the community to encourage active agri-robotics research.

Keywords:Computer Vision, Anomaly Detection, Self-supervised Learning, Deep Learning, Data Augmentation, Agri-robotics, Robotic Monitoring, Agriculture, Fruit Image Data, Robotic perception
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
H Engineering > H671 Robotics
D Veterinary Sciences, Agriculture and related subjects > D400 Agriculture
G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > Lincoln Institute for Agri-Food Technology
ID Code:46584
Deposited On:18 Oct 2021 10:57

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