Grape Cluster Real-Time Detection in Complex Natural Scenes Based on YOLOv5s Deep Learning Network

Zhang, Chuandong; Ding, Huali; Shi, Qinfeng; Wang, Yunfei

doi:10.3390/agriculture12081242

Cited by 28 publications

(17 citation statements)

References 28 publications

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“…In [14,32,33,43], only one or two classes were used as an output [44]. In relation to the more complex variations of the object of interest and the number of classes in our work, it can be concluded that the results obtained in this research are more significant because 5 classes are defined in our work and the results are promising: F1 as a measure of the proposed algorithm amounted to 92-97%, and the machine's current accuracy is 91-98%.…”

Section: Discussionmentioning

confidence: 99%

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Promotion of Color Sorting in Industrial Systems Using a Deep Learning Algorithm

et al. 2022

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Color sorting is a technological operation performed with the aim of classifying compliant and noncompliant agricultural products in large-capacity industrial systems for agricultural product processing. This paper investigates the application of the YOLOv3 algorithm on raspberry images as a method developed for the detection, localization, and classification of objects based on convolutional neural networks (CNNs). To our knowledge, this is the first time a YOLO algorithm or CNN has been used with original images from the color sorter to focus on agricultural products. Results of the F1 measure were in the 92–97% range. Images in full resolution, 1024 × 1024, produced an average detection time of 0.37 s. The impact of the hyperparameters that define the YOLOv3 model as well as the impact of the application of the chosen augmentative methods on the model are evaluated. The successful classification of stalks, which is particularly challenging due to their shape, small dimensions, and variations, was achieved. The presented model demonstrates the ability to classify noncompliant products into four classes, some of which are appropriate for reprocessing. The software, including a graphic interface that enables the real-time testing of machine learning algorithm, is developed and presented.

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Section: Discussionmentioning

confidence: 99%

“…In [43], the authors highlighted the advantages of YOLOv5 in terms of lightweight and better real-time performance. However, YOLOv3 produced exactly the same results as YOLOv5 for F1, mAP, recall, and precision.…”

Section: Discussionmentioning

confidence: 99%

Promotion of Color Sorting in Industrial Systems Using a Deep Learning Algorithm

et al. 2022

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“…It can lead to data leakage if the train/validation/test split is performed after offline augmentation. This is the case in the work of Zhang et al [114] (10-fold oversampling before splitting).…”

Section: Performance Comparisonmentioning

confidence: 91%

Computer Vision and Deep Learning for Precision Viticulture

Mohimont

Alin

Rondeau³

et al. 2022

Agronomy

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During the last decades, researchers have developed novel computing methods to help viticulturists solve their problems, primarily those linked to yield estimation of their crops. This article aims to summarize the existing research associated with computer vision and viticulture. It focuses on approaches that use RGB images directly obtained from parcels, ranging from classic image analysis methods to Machine Learning, including novel Deep Learning techniques. We intend to produce a complete analysis accessible to everyone, including non-specialized readers, to discuss the recent progress of artificial intelligence (AI) in viticulture. To this purpose, we present work focusing on detecting grapevine flowers, grapes, and berries in the first sections of this article. In the last sections, we present different methods for yield estimation and the problems that arise with this task.

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“…Moreover, due to the limitation of hardware resources, adopting a lighter network is necessary. The different network configurations of YOLOv5 model are divided into YOLOV5 37 , YOLOv5m 38 , YOLOv5l 39 , and other structures, among which YOLOV5 model has the simplest structure, so it is considered.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLOv5 infrared tank target detection method under ground background

Liang

Yan²,

Ren³

et al. 2023

Sci Rep

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The detection precision of infrared seeker directly affects the guidance precision of infrared guidance system. To solve the problem of low target detection accuracy caused by the change of imaging scale, complex ground background and inconspicuous infrared target characteristics when infrared image seeker detects ground tank targets. In this paper, a You Only Look Once, Transform Head Squeeze-and-Excitation (YOLOv5s-THSE) model is proposed based on the YOLOv5s model. A multi-head attention mechanism is added to the backbone and neck of the network, and deeper target features are extracted using the multi-head attention mechanism. The Cross Stage Partial, Squeeze-and-Exclusion module is added to the neck of the network to suppress the complex background and make the model pay more attention to the target. A small object detection head is introduced into the head of the network, and the CIoU loss function is used in the model to improve the detection accuracy of small objects and obtain more stable training regression. Through these several improvement measures, the background of the infrared target is suppressed, and the detection ability of infrared tank targets is improved. Experiments on infrared tank target datasets show that our proposed model can effectively improve the detection performance of infrared tank targets under ground background compared with existing methods, such as YOLOv5s, YOLOv5s + SE, and YOLOV 5 s + Convective Block Attention Module.

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Grape Cluster Real-Time Detection in Complex Natural Scenes Based on YOLOv5s Deep Learning Network

Cited by 28 publications

References 28 publications

Promotion of Color Sorting in Industrial Systems Using a Deep Learning Algorithm

Promotion of Color Sorting in Industrial Systems Using a Deep Learning Algorithm

Computer Vision and Deep Learning for Precision Viticulture

Improved YOLOv5 infrared tank target detection method under ground background

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