[Retracted] Fast Recognition Method for Multiple Apple Targets in Complex Occlusion Environment Based on Improved YOLOv5

Qian, Hao; Guo, Xin; Yang, Feng

doi:10.1155/2023/3609541

Cited by 10 publications

(6 citation statements)

References 14 publications

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“…[7] Lettuce image growth indices DL pred. [8] Apple images fruit maturation ML class. [9] Apple images leaf disease DL class.…”

Section: Referencementioning

confidence: 99%

“…For example, in [6] the authors try to detect leaf miners in tomato plants by applying two types of deep neural network to classify and segment plant images, while in [7] a convolutional neural network is used to predict some growth indices of lettuce plants, always using image data. In [8] the target crop regards apples and in particular the classification of their maturation degree in order to facilitate the work of picking robots. This is achieved by exploiting histograms of oriented gradients, SVM, and a fast identification technique for multiple targets very suitable for a complex occlusion environment.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Classification and Forecasting of Water Stress in Tomato Plants Using Bioristor Data

et al. 2023

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“…[7] Lettuce image growth indices DL pred. [8] Apple images fruit maturation ML class. [9] Apple images leaf disease DL class.…”

Section: Referencementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Classification and Forecasting of Water Stress in Tomato Plants Using Bioristor Data

et al. 2023

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“…In 2020, YOLOv5 [18] was introduced, shocking the world with its extremely fast detection speed, making it an ideal candidate for real-time conditions and mobile deployment environments. The related studies [19][20][21][22][23][24] have made lightweight improvements to the YOLOv5s version based on different domains.…”

Section: Introductionmentioning

confidence: 99%

Object Detection Algorithm of Transmission Lines Based on Improved YOLOv5 Framework

Zhang,

Zhou,

Shi

et al. 2024

Journal of Sensors

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Foreign objects easily attach to the transmission lines because of the various laying methods and the complex, changing environment. They have a significant impact on the safe operation capability of transmission lines if these foreign objects are not detected and removed in time. An improved YOLOv5 technique is provided to detect foreign objects in transmission lines due to the low-foreign object recognition accuracy image detection. The method first reduces the computation and memory consumption by introducing the RepConv structure, further improves the detection accuracy and speed of the model by embedding the C2F structure. This method finally is further optimized neural network by the Meta-ACON activation function. The results indicate that the average detection accuracy of the improved YOLOv5 network can reach 96.9%, which is 2.2% higher than before. Additionally, corresponding detection speed can reach 258.36 frames/second, which surpasses existing mainstream target detection models, performing better in terms of the balance of inference speed and detection accuracy. Consequently, the effectiveness and superiority of the algorithm have been proved.

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“…Alruwaili et al [17] used fast R-CNN to study tomatoes, and the final results showed that the accuracy of the RTF-RCNN proposed in the study was as high as 97.42%, which is better than traditional methods. The most common research direction is using different methods to identify different crops and ultimately obtain relatively suitable methods for identifying various crops [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Research on Red Jujubes Recognition Based on a Convolutional Neural Network

Guo

et al. 2023

Applied Sciences

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Red jujube is one of the most important crops in China. In order to meet the needs of the scientific and technological development of the jujube industry, solve the problem of poverty, realize the backward advantage, and promote economic development, smart agriculture is essential. The main objective of this study was to conduct an online detection study of unpicked red jujubes in order to detect as many red jujubes in the picture as possible while minimizing the occurrence of overfitting and underfitting. Experiments were conducted using the Histogram of Oriented Gradients + Support Vector Machine (HOG+SVM) traditional detection method and the You Only Look Once version 5 (YOLOV5) and Faster R-CNN modern deep learning detection methods. The precision, recall, and F1 score were compared to obtain a better algorithm. The study also introduced the AlexNet model with the main objective of attempting to combine it with other traditional algorithms to maximize accuracy. Labeling was used to label the training images in YOLOV5 and Faster Regions with CNN Features (Faster R-CNN) to train the machine model so that the computer recognized these features when it saw new unlabeled data in subsequent experiments. The experimental results show that in the online recognition detection of red jujubes, the YOLOV5 and Faster R-CNN algorithms performed better than the HOG + SVM algorithm, which presents precision, recall, and F1 score values of 93.55%, 82.79%, and 87.84% respectively; although the HOG + SVM algorithm was relatively quicker to perform. The precision of detection was obviously more important than the efficiency of detection in this study, so the YOLOV5 and Faster R-CNN algorithms were better than the HOG + SVM algorithm. In the experiments, the Faster R-CNN algorithm had 100% precision, 99.65% recall, an F1 score of 99.82%, and 83% non-underfitting images for the recognized images, all of which were higher than YOLOV5′s values, with 97.17% recall, an F1 score of 98.56%, and 64.42% non-underfitting. In this study, therefore, the Faster R-CNN algorithm works best.

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[Retracted] Fast Recognition Method for Multiple Apple Targets in Complex Occlusion Environment Based on Improved YOLOv5

Cited by 10 publications

References 14 publications

Classification and Forecasting of Water Stress in Tomato Plants Using Bioristor Data

Classification and Forecasting of Water Stress in Tomato Plants Using Bioristor Data

Object Detection Algorithm of Transmission Lines Based on Improved YOLOv5 Framework

Research on Red Jujubes Recognition Based on a Convolutional Neural Network

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