On Combining DeepSnake and Global Saliency for Detection of Orchard Apples

Wang, Jing; Wang, Leqi; Han, Yanling; Zhang, Yun; Zhou, Ri-Gui

doi:10.3390/app11146269

Cited by 6 publications

(7 citation statements)

References 17 publications

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“…The occlusion of branches and leaves or stems in the natural environment also affects the recognition of fruits and vegetables. Some researchers have reduced the effects of branches and leaves and separated the overlapping fruits for high recognition accuracy (Bulanon, Burks, Alchanatis, Albrigo, et al, 2009; Changhui et al, 2017; Feng et al, 2011; Jiao et al, 2020; Jing et al, 2021; D. Li et al, 2019; Lv et al, 2016; H. B. Song et al, 2013; Y. Xu et al, 2015; Zeng et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

Recognition of fruits and vegetables with similar‐color background in natural environment: A survey

Xu³

et al. 2022

Journal of Field Robotics

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Fruit images collected by picking robots in natural environments have problems such as uneven lighting, complex backgrounds, occlusion of branches and leaves, and overlapping fruits and vegetables, which greatly increases the difficulty for picking robots to accurately identify target fruits and vegetables. Meanwhile, most fruits and vegetables have similar‐color backgrounds. Compared with the ones with different colors in the background, fruits and vegetables in similar‐color backgrounds are similar to the colors of their leaves and surrounding weeds, which increases the difficulty of identification. Therefore, realizing the accurate identification of fruits and vegetables in similar‐color backgrounds is vital to realize the dynamic monitoring of the growth of fruits and vegetables and intelligent picking, which has important value and application prospects for optimizing plantation management and automated harvesting operations. The work summarized the specific characteristics of fruits and vegetables with the close‐color background and the different processing procedures in the identification process. The following methods have been summarized, for example, image acquisition, image preprocessing, feature extraction, image segmentation, and fruit and vegetable detection in the process, and some methods were compared. Finally, the current research was discussed, and a solution was proposed for the different processing steps of fruit and vegetable recognition in similar‐color backgrounds. Deep learning methods were combined with some traditional methods to identify fruits and vegetables with close‐color background, which provides references for further research in various aspects.

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Section: Discussionmentioning

confidence: 99%

Recognition of fruits and vegetables with similar‐color background in natural environment: A survey

Xu³

et al. 2022

Journal of Field Robotics

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“…In addition, there are also some works that use contour instance segmentation. Wang et al [19] proposed a dynamic adaptive overlapping object separation algorithm based on the Deep Snake [20] framework, which locates individual target fruits to determine the initial contour of DeepSnake. Finally, the target fruits are labeled based on the segmentation results of the samples.The comprehensive detection accuracy was 95.66%, and the average processing time was 0.42 s in 1036 test images…”

Section: Strawberry Instance Segmentationmentioning

confidence: 99%

“…In the current strawberry recognition work based on deep learning, there is little exploration of the strawberry contours. However, recently, in the identification of other crops, Wang et al [5] combined saliency detection and traditional color difference with real-time deep-snake [6] deep learning contour segmentation model to achieve fast detection and recognition of apple fruits. In addition, in terms of dataset work, Borrero et al released a large-scale high-resolution dataset of strawberry images, along with corresponding manually labeled instance segmentation masked images.…”

Section: Introductionmentioning

confidence: 99%

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StrawSnake: A Real-time strawberry instance segmentation network based on the contour learning approach

Guo,

Hu,

Zhao

et al. 2024

Preprint

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Fruit recognition is critical for automated harvesting systems. This paper aims to explore a high-precision, real-time method for strawberry recognition. Existing methods for strawberry recognition are mostly pixel-level and box-based. However, these methods cannot accurately or in real-time determine the position of strawberries. Therefore, this paper proposes a new contour-based real-time detection and segmentation network for strawberries, called "StrawSnake." A strawberry-specific octagonal contour is designed based on the shape of strawberries for initial strawberry contour extraction. Deep snake convolution is used for boundary feature extraction and a contour feature aggregation module is proposed, prioritizing important boundary feature channels and enhancing the global context understanding capability of strawberries by aggregating multi-level strawberry contour features. This study obtained a new high-resolution dataset of 3100 strawberry images and the corresponding manually segmented ground truth images (StrawDI_Db1 database). Edge detection was used to label the ground truth images of strawberry contours in the dataset to build strawberry contour labels. Results obtained from testing 200 images in the dataset show that our StrawSnake can achieve real-time strawberry contour recognition at a speed of 39.2 frames per second, with an average accuracy of 59.23%. On another TongStraw_DB built by us, compared with other methods,, our StrawSnake algorithm has also a competitive advantage in terms of segmentation speed and accuracy. Finally, through ablation experiments, we demonstrate the performance improvement brought by DSConv and MFRB. Since our method can be integrated into strawberry automated harvesting systems, it is considered the most promising approach.

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“…Numerous scholars across the world have conducted extensive research on target object recognition technology [10][11][12][13]. In the field of fruit crop detection in natural environments, feature extraction and recognition have predominately targeted tomato [14,15], apple [16][17][18], cucumber [19,20], strawberry [21], sugarcane [22], pineapple [23], and various other fruits. Among the various fruits, the planting area and yield of kiwifruit in particular have continued to increase over time.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Detection Algorithm of Kiwifruit Based on Improved YOLOX-S

Zhou

Zou

et al. 2022

Agriculture

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Considering the high requirements of current kiwifruit picking recognition systems for mobile devices, including the small number of available features for image targets and small-scale aggregation, an enhanced YOLOX-S target detection algorithm for kiwifruit picking robots is proposed in this study. This involved designing a new multi-scale feature integration structure in which, with the aim of providing a small and lightweight model, the feature maps used for detecting large targets in the YOLOX model are eliminated, the feature map of small targets is sampled through the nearest neighbor values, the superficial features are spliced with the final features, the gradient of the SiLU activation function is perturbed, and the loss function at the output is optimized. The experimental results show that, compared with the original YOLOX-S, the enhanced model improved the detection average precision (AP) of kiwifruit images by 6.52%, reduced the number of model parameters by 44.8%, and improved the model detection speed by 63.9%. Hence, with its outstanding effectiveness and relatively light weight, the proposed model is capable of effectively providing data support for the 3D positioning and automated picking of kiwifruit. It may also successfully provide solutions in similar fields related to small target detection.

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On Combining DeepSnake and Global Saliency for Detection of Orchard Apples

Cited by 6 publications

References 17 publications

Recognition of fruits and vegetables with similar‐color background in natural environment: A survey

Recognition of fruits and vegetables with similar‐color background in natural environment: A survey

StrawSnake: A Real-time strawberry instance segmentation network based on the contour learning approach

Lightweight Detection Algorithm of Kiwifruit Based on Improved YOLOX-S

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