Detection of maturity stages of coconuts in complex background using Faster R-CNN model

Parvathi, S.; Selvi, S. Tamil

doi:10.1016/j.biosystemseng.2020.12.002

Cited by 98 publications

(41 citation statements)

References 19 publications

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“…The results show average mean accuracy of 83.34% on the actual test dataset. In [17] the improved Faster R-CNN algorithm with the ResNet-50 network is proposed for the detection of maturity stages for coconuts. The detection performance achieved using the improved Faster R-CNN model was better than that single-shot detector (SSD), YOLO-V3 and Region-based Fully Convolutional Networks (R-FCN).…”

Section: Scientific Publicationsmentioning

confidence: 99%

“…Yan et al [15] RGB video YOLOv5s, YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 Zhang et al [16] RGB image Faster R-CNN Parvathi et al [17] RGB image Faster R-CNN, SSD, YOLOv3, R-FCN Lawal et al [18] RGB images YOLOv3 modified: YOLO-Tomato-A, YOLO-Tomato-B and YOLO-Tomato-C Itakura et al [19] RGB video YOLOv2 Perez-Borrero et.al. [20] RGB video Mask R-CNN Apolo Apolo et al [21] RGB images Faster R-CNN Santos et al [22] RGB images Mask R-CNN, YOLOv2 and YOLOv3…”

Section: Article Data Type Modelmentioning

confidence: 99%

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Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

et al. 2021

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Raspberries are fruit of great importance for human beings. Their products are segmented by quality. However, estimating raspberry quality is a manual process carried out at the reception of the fruit processing plant, and is thus exposed to factors that could distort the measurement. The agriculture industry has increased the use of deep learning (DL) in computer vision systems. Non-destructive and computer vision equipment and methods are proposed to solve the problem of estimating the quality of raspberries in a tray. To solve the issue of estimating the quality of raspberries in a picking tray, prototype equipment is developed to determine the quality of raspberry trays using computer vision techniques and convolutional neural networks from images captured in the visible RGB spectrum. The Faster R–CNN object-detection algorithm is used, and different pretrained CNN networks are evaluated as a backbone to develop the software for the developed equipment. To avoid imbalance in the dataset, an individual object-detection model is trained and optimized for each detection class. Finally, both hardware and software are effectively integrated. A conceptual test is performed in a real industrial scenario, thus achieving an automatic evaluation of the quality of the raspberry tray, in this way eliminating the intervention of the human expert and eliminating errors involved in visual analysis. Excellent results were obtained in the conceptual test performed, reaching in some cases precision of 100%, reducing the evaluation time per raspberry tray image to 30 s on average, allowing the evaluation of a larger and representative sample of the raspberry batch arriving at the processing plant.

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Section: Scientific Publicationsmentioning

confidence: 99%

Section: Article Data Type Modelmentioning

confidence: 99%

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

et al. 2021

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“…Facial landmark detection was made through facial patches along with [24]. Faster R-CNN algorithm is used to detect the tender coconut in the tree and proposed that it is better than SSD and YOLO [25].…”

Section: Introductionmentioning

confidence: 99%

Automated object detection of mechanical fasteners using faster region based convolutional neural networks

Karthikeyan

Subashini

2021

IJECE

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Mechanical fasteners are widely used in manufacturing of hardware and mechanical components such as automobiles, turbine & power generation and industries. Object detection method play a vital role to make a smart system for the society. Internet of things (IoT) leads to automation based on sensors and actuators not enough to build the systems due to limitations of sensors. Computer vision is the one which makes IoT too much smarter using deep learning techniques. Object detection is used to detect, recognize and localize the object in an image or a real time video. In industry revolution, robot arm is used to fit the fasteners to the automobile components. This system will helps the robot to detect the object of fasteners such as screw and nails accordingly to fit to the vehicle moved in the assembly line. Faster R-CNN deep learning algorithm is used to train the custom dataset and object detection is used to detect the fasteners. Region based convolutional neural networks (Faster R-CNN) uses a region proposed network (RPN) network to train the model efficiently and also with the help of Region of Interest able to localize the screw and nails objects with a mean average precision of 0.72 percent leads to accuracy of 95 percent object detection

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“…Two-stage algorithms require, first, forming a regional proposal, and then the object is classified and localized using convolutional neural networks (CNN). Typical algorithms are Faster-RCNN [15][16][17][18], Mask R-CNN [19], etc. Their recognition speed is slow due to the requirement of multiple detection and classification.…”

Section: Introductionmentioning

confidence: 99%

Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network

Fang

et al. 2021

Agronomy

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A consistent orientation of ginger shoots when sowing ginger is more conducive to high yields and later harvesting. However, current ginger sowing mainly relies on manual methods, seriously hindering the ginger industry’s development. Existing ginger seeders still require manual assistance in placing ginger seeds to achieve consistent ginger shoot orientation. To address the problem that existing ginger seeders have difficulty in automating seeding and ensuring consistent ginger shoot orientation, this study applies object detection techniques in deep learning to the detection of ginger and proposes a ginger recognition network based on YOLOv4-LITE, which, first, uses MobileNetv2 as the backbone network of the model and, second, adds coordinate attention to MobileNetv2 and uses Do-Conv convolution to replace part of the traditional convolution. After completing the prediction of ginger and ginger shoots, this paper determines ginger shoot orientation by calculating the relative positions of the largest ginger shoot and the ginger. The mean average precision, Params, and giga Flops of the proposed YOLOv4-LITE in the test set reached 98.73%, 47.99 M, and 8.74, respectively. The experimental results show that YOLOv4-LITE achieved ginger seed detection and ginger shoot orientation calculation, and that it provides a technical guarantee for automated ginger seeding.

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Detection of maturity stages of coconuts in complex background using Faster R-CNN model

Cited by 98 publications

References 19 publications

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

Automated object detection of mechanical fasteners using faster region based convolutional neural networks

Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network

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