论文标题
在具有挑战性的条件下的Kiwifruit检测
Kiwifruit detection in challenging conditions
论文作者
论文摘要
准确且可靠的Kiwifruit检测是开发选择性水果收获机器人的最大挑战之一。果园机器人的视觉系统面临着困难，例如动态照明条件和果实的阻塞。本文提出了一种语义分割方法，采用两种新型的图像预科技术，旨在检测树冠中发现的恶劣照明条件下的猕猴桃。在不同的照明条件下（典型，眩光和过度曝光），在3D现实世界图像集上评估了呈现的系统的性能。单独使用语义分割方法在典型的照明图像集上达到0.82的F1_SCORE，但在刺激性的照明方面挣扎，F1_SCORE为0.13。利用预性技术在严酷的照明下视觉系统提高到F1_SCORE 0.42。为了应对水果闭塞挑战，发现整体方法能够检测到所有照明条件下的87.0％的非封闭式kiwifruit。
Accurate and reliable kiwifruit detection is one of the biggest challenges in developing a selective fruit harvesting robot. The vision system of an orchard robot faces difficulties such as dynamic lighting conditions and fruit occlusions. This paper presents a semantic segmentation approach with two novel image prepossessing techniques designed to detect kiwifruit under the harsh lighting conditions found in the canopy. The performance of the presented system is evaluated on a 3D real-world image set of kiwifruit under different lighting conditions (typical, glare, and overexposed). Alone the semantic segmentation approach achieves an F1_score of 0.82 on the typical lighting image set, but struggles with harsh lighting with an F1_score of 0.13. Utilising the prepossessing techniques the vision system under harsh lighting improves to an F1_score 0.42. To address the fruit occlusion challenge, the overall approach was found to be capable of detecting 87.0% of non-occluded and 30.0% of occluded kiwifruit across all lighting conditions.
