■随着劳动力成本的不断增加,农业部门迫切需要自动化的苹果-QPick设备。在苹果收获之前,该设备不仅要准确地定位苹果,而且还能辨别水果的可抓握性。虽然已经进行了许多关于苹果检测的研究,与确定苹果可抓取性有关的挑战仍未解决。
■本研究介绍了一种基于增强的YOLOv5s模型检测多遮挡苹果的方法,目的是在复杂的果园环境中识别苹果闭塞的类型并确定苹果的可抓性。使用引导自己的atent(BYOL)和知识转移(KT)策略,我们有效地提高了多遮挡苹果的分类精度,同时降低了数据生产成本。还包含选择性内核(SK)模块,使网络模型能够更精确地识别各种苹果遮挡类型。为了评估我们网络模型的性能,我们定义了三个关键指标:APGA,APTUGA,和APUGA,表示可抓取的平均检测精度,暂时无法抓住,还有抓不住的苹果,分别。
■实验结果表明,改进的YOLOv5s模型表现得非常好,达到94.78%的检测精度,93.86%,APGA为94.98%,APTUGA,和APUGA,分别。
■与当前的轻量网络模型(如YOLOX-s和YOLOv7)相比,我们提出的方法在多个评估指标上显示出显著的优势。在未来的研究中,我们打算将水果姿势和咬合检测相结合,以f]进一步提高苹果采摘设备的视觉感知能力。
UNASSIGNED: With continuously increasing labor costs, an urgent need for automated apple- Qpicking equipment has emerged in the agricultural sector. Prior to apple harvesting, it is imperative that the equipment not only accurately locates the apples, but also discerns the graspability of the fruit. While numerous studies on apple detection have been conducted, the challenges related to determining apple graspability remain unresolved.
UNASSIGNED: This study introduces a method for detecting multi-occluded apples based on an enhanced
YOLOv5s model, with the aim of identifying the type of apple occlusion in complex orchard environments and determining apple graspability. Using bootstrap your own atent(BYOL) and knowledge transfer(KT) strategies, we effectively enhance the classification accuracy for multi-occluded apples while reducing data production costs. A selective kernel (SK) module is also incorporated, enabling the network model to more precisely identify various apple occlusion types. To evaluate the performance of our network model, we define three key metrics: APGA, APTUGA, and APUGA, representing the average detection accuracy for graspable, temporarily ungraspable, and ungraspable apples, respectively.
UNASSIGNED: Experimental results indicate that the improved
YOLOv5s model performs exceptionally well, achieving detection accuracies of 94.78%, 93.86%, and 94.98% for APGA, APTUGA, and APUGA, respectively.
UNASSIGNED: Compared to current lightweight network models such as YOLOX-s and YOLOv7s, our proposed method demonstrates significant advantages across multiple evaluation metrics. In future research, we intend to integrate fruit posture and occlusion detection to f]urther enhance the visual perception capabilities of apple-picking equipment.