A Visual Detection Method for Multiple Kinds of Camellia Oleifera Fruit Picking Robots

Wang, Jinpeng; Zhou, Jialiang; He, Mingyi; Gu, Haiyang; Zhou, Lei; Zhou, Hu

doi:10.2139/ssrn.4482152

Cited by 1 publication

(2 citation statements)

References 7 publications

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“…The angle loss calculation is applied to the distance loss calculation by redefining the distance loss ∆, as shown in Equation (7).…”

Section: Loss Function Improvementmentioning

confidence: 99%

“…Experiments showed that the precision of the improved model was 94.00%, and the mean average precision at 0.5 (mAP@0.5) was 94.37%, which could meet the requirements of the harvesting robot for detecting Camellia oleifera fruit at night [6]. Wang et al used Mask R CNN algorithm to detect Camellia oleifera fruit in natural scenes, and the results showed that the segmentation accuracy of the model was 89.85%, and the mAP@0.5 was 89.42% [7]. Song et al conducted a study on the detection method of Camellia oleifera fruit in the natural environment using YOLOv5s, and the experiments showed that the precision was 90.73%, the F 1 score was 94.4%, the mAP@0.5 was 98.71%, the single image detection time was 12.7 ms, and the model weight was 14.08 MB [8].…”

Section: Introductionmentioning

confidence: 99%

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Camellia oleifera Fruit Detection Algorithm in Natural Environment Based on Lightweight Convolutional Neural Network

Li,

Kang,

Rao

et al. 2023

Applied Sciences

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At present, Camellia oleifera fruit harvesting relies on manual labor with low efficiency, while mechanized harvesting could result in bud damage because flowering and fruiting are synchronized. As a prerequisite, rapid detection and identification are urgently needed for high accuracy and efficiency with simple models to realize selective and intelligent harvesting. In this paper, a lightweight detection algorithm YOLOv5s-Camellia based on YOLOv5s is proposed. First, the network unit of the lightweight network ShuffleNetv2 was used to reconstruct the backbone network, and thereby the number of computations and parameters of the model was reduced to increase the running speed for saving computational costs. Second, to mitigate the impact of the lightweight improvement on model detection accuracy, three efficient channel attention (ECA) modules were introduced into the backbone network to enhance the network’s attention to fruit features, and the Concat operation in the neck network was replaced by the Add operation with fewer parameters, which could increase the amount of information under features while maintaining the same number of channels. Third, the Gaussian Error Linear Units (GELU) activation function was introduced to improve the nonlinear characterization ability of the network. In addition, to improve the ability of the network to locate objects in the natural environment, the penalty index was redefined to optimize the bounding box loss function, which can improve the convergence speed and regression accuracy. Furthermore, the final experimental results showed that this model possesses 98.8% accuracy, 5.5G FLOPs computation, and 6.3MB size, and the detection speed reached 60.98 frame/s. Compared with the original algorithm, the calculation amount, size, and parameters were reduced by 65.18%, 56.55%, and 57.59%, respectively. The results can provide a technical reference for the development of a Camellia oleifera fruit-harvesting robot.

show abstract

“…The angle loss calculation is applied to the distance loss calculation by redefining the distance loss ∆, as shown in Equation (7).…”

Section: Loss Function Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Camellia oleifera Fruit Detection Algorithm in Natural Environment Based on Lightweight Convolutional Neural Network

Li,

Kang,

Rao

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

A Visual Detection Method for Multiple Kinds of Camellia Oleifera Fruit Picking Robots

Cited by 1 publication

References 7 publications

Camellia oleifera Fruit Detection Algorithm in Natural Environment Based on Lightweight Convolutional Neural Network

Camellia oleifera Fruit Detection Algorithm in Natural Environment Based on Lightweight Convolutional Neural Network

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