YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3
YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3
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DOI：
10.3390/s20072145
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发表时间：
2020-04-01
期刊：
影响因子：
3.9
通讯作者：
Kim, Jae Ho
中科院分区：
文献类型：
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作者：
Liu, Guoxu;Nouaze, Joseph Christian;Kim, Jae Ho
自动水果检测是采摘机器人的一个非常重要的好处。然而，复杂的环境条件，如光照变化、枝叶遮挡以及番茄重叠等，给水果检测带来了很大的挑战。在本研究中，基于YOLOv3，提出了一种改进的番茄检测模型YOLO-Tomato来处理这些问题。 YOLOv3中融入了密集的架构，以方便特征的重用，并有助于学习更紧凑、更准确的模型。此外，该模型用圆形边界框（C-Bbox）代替了传统的矩形边界框（R-Bbox）来进行番茄定位。新的边界框可以更精确地匹配西红柿，从而改进非极大值抑制（NMS）的交并（IoU）计算。它们还减少了预测坐标。消融研究证明了这些修改的功效。 YOLO-Tomato 与几种最先进的检测方法进行了比较，它具有最好的检测性能。
Automatic fruit detection is a very important benefit of harvesting robots. However, complicated environment conditions, such as illumination variation, branch, and leaf occlusion as well as tomato overlap, have made fruit detection very challenging. In this study, an improved tomato detection model called YOLO-Tomato is proposed for dealing with these problems, based on YOLOv3. A dense architecture is incorporated into YOLOv3 to facilitate the reuse of features and help to learn a more compact and accurate model. Moreover, the model replaces the traditional rectangular bounding box (R-Bbox) with a circular bounding box (C-Bbox) for tomato localization. The new bounding boxes can then match the tomatoes more precisely, and thus improve the Intersection-over-Union (IoU) calculation for the Non-Maximum Suppression (NMS). They also reduce prediction coordinates. An ablation study demonstrated the efficacy of these modifications. The YOLO-Tomatowas compared to several state-of-the-art detection methods and it had the best detection performance.