  Automated apple harvesting has attracted significant research interest in
recent years due to its potential to revolutionize the apple industry,
addressing the issues of shortage and high costs in labor. One key technology
to fully enable efficient automated harvesting is accurate and robust apple
detection, which is challenging due to complex orchard environments that
involve varying lighting conditions and foliage/branch occlusions. Furthermore,
clustered apples are common in the orchard, which brings additional challenges
as the clustered apples may be identified as one apple. This will cause issues
in localization for subsequent robotic operations. In this paper, we present
the development of a novel deep learning-based apple detection framework,
Occluder-Occludee Relational Network (O2RNet), for robust detection of apples
in such clustered environments. This network exploits the occuluder-occludee
relationship modeling head by introducing a feature expansion structure to
enable the combination of layered traditional detectors to split clustered
apples and foliage occlusions. More specifically, we collect a comprehensive
apple orchard image dataset under different lighting conditions (overcast,
front lighting, and back lighting) with frequent apple occlusions. We then
develop a novel occlusion-aware network for apple detection, in which a feature
expansion structure is incorporated into the convolutional neural networks to
extract additional features generated by the original network for occluded
apples. Comprehensive evaluations are performed, which show that the developed
O2RNet outperforms state-of-the-art models with a higher accuracy of 94\% and a
higher F1-score of 0.88 on apple detection.
