Study on Pear Flowers Detection Performance of YOLO-PEFL Model Trained With Synthetic Target Images

Chen, Zhong; Wang, Yawei; Liu, Suchwen; Lin, Guimin; He, Peng; Zhang, Zhaoguo; Zhou, Yi

doi:10.3389/fpls.2022.911473

Cited by 10 publications

(11 citation statements)

References 37 publications

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“…YOLO (You Only Look Once) is a one-stage detection network that converts object detection into a regression problem using convolutional neural networks (Wang et al, , 2022. YOLO v5, the latest version of the YOLO model (Jocher and Stoken, 2021), has a faster detection speed and higher accuracy than the previous version.…”

Section: Detection Of the 2d Bounding Box Of Citrus Fruitmentioning

confidence: 99%

Detection and localization of citrus fruit based on improved You Only Look Once v5s and binocular vision in the orchard

et al. 2022

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Intelligent detection and localization of mature citrus fruits is a critical challenge in developing an automatic harvesting robot. Variable illumination conditions and different occlusion states are some of the essential issues that must be addressed for the accurate detection and localization of citrus in the orchard environment. In this paper, a novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed. First, a new loss function (polarity binary cross-entropy with logit loss) for YOLO v5s is designed to calculate the loss value of class probability and objectness score, so that a large penalty for false and missing detection is applied during the training process. Second, to recover the missing depth information caused by randomly overlapping background participants, Cr-Cb chromatic mapping, the Otsu thresholding algorithm, and morphological processing are successively used to extract the complete shape of the citrus, and the kriging method is applied to obtain the best linear unbiased estimator for the missing depth value. Finally, the citrus spatial position and posture information are obtained according to the camera imaging model and the geometric features of the citrus. The experimental results show that the recall rates of citrus detection under non-uniform illumination conditions, weak illumination, and well illumination are 99.55%, 98.47%, and 98.48%, respectively, approximately 2–9% higher than those of the original YOLO v5s network. The average error of the distance between the citrus fruit and the camera is 3.98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization.

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Section: Detection Of the 2d Bounding Box Of Citrus Fruitmentioning

confidence: 99%

Detection and localization of citrus fruit based on improved You Only Look Once v5s and binocular vision in the orchard

et al. 2022

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“…Furthermore, Immaneni et al [44] tested YOLOV4 [45] drone images from a strawberry field and achieved a better accuracy (91.95% at 14.6 FPS). Related species such as Pear flowers, have also shown promising results with Yolo (mAP of 94%) [46].…”

Section: B Camera Characterizationmentioning

confidence: 99%

See as a Bee: UV Sensor for Aerial Strawberry Crop Monitoring

Heath¹,

Imran²,

Starobinski³

2022

Preprint

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Precision agriculture aims to use technological tools for the agro-food sector to increase productivity, cut labor costs, and reduce the use of resources. This work takes inspiration from bees' vision to design a remote sensing system tailored to incorporate UV-reflectance into a flower detector. We demonstrate how this approach can provide feature-rich images for deep learning strawberry flower detection and we apply it to a scalable, yet cost effective aerial monitoring robotic system in the field. We also compare the performance of our UV-G-B image detector with a similar work that utilizes RGB images.

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“…(2022) developed an efficient tomato picking robot based on traditional image processing methods and YOLOv5 object detection algorithm, which had high detection accuracy under different lighting conditions, with an average deviation of 2 mm and a picking time of 9 s/cluster. Wang et al. (2022) proposed an improved YOLOv4 model for pear detection in the natural environment.…”

Section: Introductionmentioning

confidence: 99%

Dragon fruit detection in natural orchard environment by integrating lightweight network and attention mechanism

et al. 2022

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An improved lightweight network (Improved YOLOv5s) was proposed based on YOLOv5s in this study to realise all-weather detection of dragon fruit in a complex orchard environment. A ghost module was introduced in the original YOLOv5s to realise the lightweight of the model. The coordinate attention mechanism was joined to make the model accurately locate and identify the dense dragon fruits. A bidirectional feature pyramid network was built to improve the detection effect of dragon fruit at different scales. SIoU loss function was adopted to improve the convergence speed during model training. The improved YOLOv5s model was used to detect a dragon fruit dataset collected in the natural environment. Results showed that the mean average precision (mAP), precision (P) and recall (R) of the model was 97.4%, 96.4% and 95.2%, respectively. The model size, parameters (Params) and floating-point operations (FLOPs) were 11.5 MB, 5.2 M and 11.4 G, respectively. Compared with the original YOLOv5s network, the model size, Params and FLOPs of the improved model was reduced by 20.6%, 18.75% and 27.8%, respectively. Meanwhile, the mAP of the improved model was improved by 1.1%. The results prove that the improved model had a more lightweight structure and better detection performance. Moreover, the average precision (AP) of the improved YOLOv5s for dragon fruit under the front light, back light, side light, cloudy day and night was 99.5%, 97.3%, 98.5%, 95.5% and 96.1%, respectively. The detection performance met the requirements of all-weather detection of dragon fruit and the improved model had good robustness. This study provides a theoretical basis and technical support for fruit monitoring based on unmanned aerial vehicle technology and intelligent picking based on picking robot technology.

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Study on Pear Flowers Detection Performance of YOLO-PEFL Model Trained With Synthetic Target Images

Cited by 10 publications

References 37 publications

Detection and localization of citrus fruit based on improved You Only Look Once v5s and binocular vision in the orchard

Detection and localization of citrus fruit based on improved You Only Look Once v5s and binocular vision in the orchard

See as a Bee: UV Sensor for Aerial Strawberry Crop Monitoring

Dragon fruit detection in natural orchard environment by integrating lightweight network and attention mechanism

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