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

TL;DR

This paper introduces Channel Randomisation, a novel data augmentation technique that enhances self-supervised learning for fruit anomaly detection by focusing on colour irregularities, leading to more reliable robotic monitoring.

Contribution

The paper proposes Channel Randomisation, a new augmentation method that emphasizes colour features over structural cues for improved anomaly detection in agricultural robotics.

Findings

Channel Randomisation improves anomaly detection accuracy across fruit species.

Validation accuracy correlates with actual detection performance, enabling early stopping.

The approach is validated on a new dataset of 3.5K strawberry images.

Abstract

Data augmentation can be a simple yet powerful tool for autonomous robots to fully utilise available data for selfsupervised identification of atypical scenes or objects. State-of-the-art augmentation methods arbitrarily embed "structural" peculiarity on typical images so that classifying these artefacts can provide guidance for learning representations for the detection of anomalous visual signals. 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 could be 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 networks 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) this colour-based alternative can better learn representations for consistently accurate identification of fruit anomalies in various fruit species, and also, (2) unlike other methods, the validation accuracy can be utilised as a criterion for early stopping of training in practice due to positive correlation between the performance in the self-supervised colour-differentiation task and the subsequent detection rate of actual anomalous fruits. Also, the proposed approach is evaluated on a new agricultural dataset, Riseholme-2021, consisting of 3.5K strawberry images gathered by a mobile robot, which we share online to encourage active agri-robotics research.