The advancement of agricultural robotics holds immense promise for
transforming fruit harvesting practices, particularly within the apple
industry. The accurate detection and localization of fruits are pivotal for the
successful implementation of robotic harvesting systems. In this paper, we
propose a novel approach to apple detection and position estimation utilizing
an object detection model, YOLOv5. Our primary objective is to develop a robust
system capable of identifying apples in complex orchard environments and
providing precise location information. To achieve this, we curated an
autonomously labeled dataset comprising diverse apple tree images, which was
utilized for both training and evaluation purposes. Through rigorous
experimentation, we compared the performance of our YOLOv5-based system with
other popular object detection models, including SSD. Our results demonstrate
that the YOLOv5 model outperforms its counterparts, achieving an impressive
apple detection accuracy of approximately 85%. We believe that our proposed
system's accurate apple detection and position estimation capabilities
represent a significant advancement in agricultural robotics, laying the
groundwork for more efficient and sustainable fruit harvesting practices.

通过使用 YOLOv5 模型和多样的苹果树图像数据集，我们提出了一种新颖的苹果检测和位置估计方法，实现了在复杂果园环境中准确识别苹果并提供精确位置信息的鲁棒系统，其具有约 85% 的苹果检测准确率，这在农业机器人领域取得了重要进展，为更高效、可持续的水果采摘实践奠定了基础。

基于 YOLOv5 的果园中精确的苹果检测与定位用于机器采摘系统

Precise Apple Detection and Localization in Orchards using YOLOv5 for  Robotic Harvesting Systems

Crop detection is integral for precision agriculture applications such as
automated yield estimation or fruit picking. However, crop detection, e.g.,
apple detection in orchard environments remains challenging due to a lack of
large-scale datasets and the small relative size of the crops in the image. In
this work, we address these challenges by reformulating the apple detection
task in a semi-supervised manner. To this end, we provide the large,
high-resolution dataset MAD comprising 105 labeled images with 14,667 annotated
apple instances and 4,440 unlabeled images. Utilizing this dataset, we also
propose a novel Semi-Supervised Small Apple Detection system S$^3$AD based on
contextual attention and selective tiling to improve the challenging detection
of small apples, while limiting the computational overhead. We conduct an
extensive evaluation on MAD and the MSU dataset, showing that S$^3$AD
substantially outperforms strong fully-supervised baselines, including several
small object detection systems, by up to $14.9\%$. Additionally, we exploit the
detailed annotations of our dataset w.r.t. apple properties to analyze the
influence of relative size or level of occlusion on the results of various
systems, quantifying current challenges.

本研究提出了一种半监督的小苹果检测系统 (S^3AD)，通过利用大型高分辨率数据集 MAD，基于上下文关注和选择性平铺的方法，解决了水果检测中小型水果检测的困难，同时限制了计算开销，并在评估中表现出了明显优势。