&amp;lt;i&amp;gt;Rapid detection of fruits in orchard scene based on deep neural network&amp;lt;/i&amp;gt;

Tao, Yongting; Zhou, Jun; Wang, Kai; Shen, Wenlong

doi:10.13031/aim.201801055

Cited by 7 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, Faster RCNN is popular for its good performance. It has been used to detect sweet pep- pers [8] and apples [9]. In addition, a Faster RCNN model for detecting varieties of fruits was constructed and trained [10].…”

Section: Introductionmentioning

confidence: 99%

A Detection Method for Apple Fruits Based on Color and Shape Features

Liu

Jia

et al. 2019

IEEE Access

View full text Add to dashboard Cite

The skins of most mature apple fruits are incompletely red and also include green and pale yellow color, which increases the difficulty of fruit detection by machine vision. A detection method based on color and shape features is proposed for this kind of apple fruits. Simple linear iterative clustering (SLIC) is adapted to segment images taken in orchards into super-pixel blocks. The color feature extracted from blocks is used to determine candidate regions, which can filter a large proportion of non-fruit blocks and improve detection precision. Next, the histogram of oriented gradient (HOG) is adopted to describe the shape of fruits, which is applied to detect fruits in candidate regions and locate the position of fruits further. The proposed method was tested by images taken under different illuminations. The average values of recall, precision, and F 1 reach 89.80%, 95.12%, and 92.38% respectively. The performance of detecting fruits covered at different levels is also tested. The values of the recall are all more than 85%, which indicates that proposed method can detect a great part of covered fruits. Compared with pedestrian detection method and faster region-based convolutional neural network (RCNN), the proposed method has the best performance and higher than faster RCNN slightly. However, the proposed method is not robust to noise and its elapsed time of one image is 1.94 s and less than faster RCNN.

show abstract

Section: Introductionmentioning

confidence: 99%

A Detection Method for Apple Fruits Based on Color and Shape Features

Liu

Jia

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Different types of CNNs have been applied to detect a variety of fruits. Tao et al [18] adopted Faster RCNN to detect peaches, apples and oranges. In this study, two kinds of CNNs, ZFnet and VGG16, were used as the backbone network of Faster RCNN to detect all kinds of fruits mentioned above respectively and the detection precisions were all more than 90%.…”

Section: Introductionmentioning

confidence: 99%

Cucumber Fruits Detection in Greenhouses Based on Instance Segmentation

et al. 2019

View full text Add to dashboard Cite

The cucumber fruits have the same color with leaves and their shapes are all long and narrow, which is different from other common fruits, such as apples, tomatoes, and strawberries, etc. Therefore, cucumber fruits are more difficult to be detected by machine vision in greenhouses for special color and shape. A pixel-wise instance segmentation method, mask region-based convolutional neural network (Mask RCNN) of an improved version, is proposed to detect cucumber fruits. Resnet-101 is selected as the backbone of Mask RCNN with feature pyramid network (FPN). To improve the detection precision, region proposal network (RPN) in original Mask RCNN is improved. Logical green (LG) operator is designed to filter nongreen background and limit the range of anchor boxes. Besides, the scales and aspect ratios of anchor boxes are also adjusted to fit the size and shape of fruits. Improved Mask RCNN has a better performance on test images. The test results are compared with that of original Mask RCNN, Faster RCNN, you only look once (YOLO) V2 and YOLO V3. The F 1 score of improved Mask RCNN in test results reaches 89.47%, which is higher than the other methods. The average elapsed time of improved Mask RCNN is 0.3461 s, which is only lower than the original Mask RCNN. Meanwhile, the mean value and standard deviation of location deviation in improved Mask RCNN are 2.10 pixels and 1.73 pixels respectively, which are lower than the other methods.

show abstract

“…Desde entonces, la bibliografía muestra que se ha continuado con el desarrollo de sistemas robóticos (Recce et al, 1996;Muscato et al, 2005;Bulanon et al, 2009;Mehta y Burks, 2014;Qiang et al, 2014). A su vez, se ha avanzado en los estudios relacionados con los sistemas de visión y la inteligencia artificial para solucionar uno de los principales problemas que se dan en este tipo de sistemas robóticos y es la dificultad para detectar la fruta en el árbol, bien por la semejanza de color entre la misma fruta y las hojas o por la oclusión parcial o total de la fruta (Annamalai et al, 2004;Kurtulmus et al, 2011;Sengupta y Lee, 2014;Choi et al, 2015;Chen et al, 2017;Peng et al, 2018;Tao et al, 2018;Pérez-Ruiz et al, 2021).…”

Section: Recolección Mecanizada De Cítricosunclassified

Estudio y desarrollo de soluciones mecanizadas automatizadas mediante la aplicación de sensores y nuevas tecnologías para la producción de cítricos

González¹

View full text Add to dashboard Cite

Las principales contribuciones de esta investigación se reflejan en la transferencia tecnológica a través de la creación de la herramienta digital inscrita en el registro de propiedad intelectual y en la publicación de los resultados en revistas científicas indexadas en el JCR, en revistas técnicas y de divulgación, en congresos nacionales e internacionales y en capítulos de libros de carácter internacional.

show abstract

<i>Rapid detection of fruits in orchard scene based on deep neural network</i>

Cited by 7 publications

References 0 publications

A Detection Method for Apple Fruits Based on Color and Shape Features

A Detection Method for Apple Fruits Based on Color and Shape Features

Cucumber Fruits Detection in Greenhouses Based on Instance Segmentation

Estudio y desarrollo de soluciones mecanizadas automatizadas mediante la aplicación de sensores y nuevas tecnologías para la producción de cítricos

Contact Info

Product

Resources

About