DeepApple: Deep Learning-based Apple Detection using a Suppression Mask R-CNN

TL;DR

DeepApple introduces a novel suppression Mask R-CNN for robust apple detection in complex orchard environments, achieving higher accuracy and faster detection times, thus advancing automated robotic harvesting technology.

Contribution

The paper presents a new deep learning framework with a suppression branch that improves apple detection accuracy under challenging conditions.

Findings

Higher F1-score of 0.905 compared to existing models

Detection time of 0.25 seconds per frame

Effective in varying lighting and occlusion conditions

Abstract

Robotic apple harvesting has received much research attention in the past few years due to growing shortage and rising cost in labor. One key enabling technology towards automated harvesting is accurate and robust apple detection, which poses great challenges as a result of the complex orchard environment that involves varying lighting conditions and foliage/branch occlusions. This letter reports on the development of a novel deep learning-based apple detection framework named DeepApple. Specifically, we first collect a comprehensive apple orchard dataset for 'Gala' and 'Blondee' apples, using a color camera, under different lighting conditions (sunny vs. overcast and front lighting vs. back lighting). We then develop a novel suppression Mask R-CNN for apple detection, in which a suppression branch is added to the standard Mask R-CNN to suppress non-apple features generated by the original network. Comprehensive evaluations are performed, which show that the developed suppression Mask R-CNN network outperforms state-of-the-art models with a higher F1-score of 0.905 and a detection time of 0.25 second per frame on a standard desktop computer.