Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT

Parico, Addie Ira Borja; Ahamed, Tofael

doi:10.3390/s21144803

Cited by 128 publications

(68 citation statements)

References 41 publications

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“…In [12] the authors propose to produce a real-time pear counter system for mobile applications using RGB video and the variants of the YOLOv4 object detector model and the multiple object-tracking algorithm Deep SORT, obtaining an accuracy of 98% with a AP@0.50 with YOLOv4-CSP. They obtained better computational cost and speed with YOLOv4-tiny, concluding that the best model is the YOLOv4 in accuracy and speed.…”

Section: Scientific Publicationsmentioning

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%

“…Parico et al [12] RGB video YOLOv4, YOLOv4-CSP, YOLOv4-tiny. Nasirahmadi et al [13] RGB video YOLO v4, R-FCN and Faster R-CNN Wang et al [14] RGB video YOLOv3…”

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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“…In a citrus detection study, a YOLO v4 model was able to detect the fruit with an accuracy of 96% using a Kinect v2 camera. In another study, pear fruit detection and counting using different YOLO v4 models (i.e., YOLO v4, YOLO v4-CSP, YOLO v4-tiny) resulted in achieved average precision of 98% [35]. Furthermore, [31] reported that YOLO v4 was able to detect apple fruit in a complex environment with recall and an average precision of around 93 and 88%, respectively, compared to Faster R-CNN with 90 and 83%, respectively.…”

Section: Resultsmentioning

confidence: 99%

Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

2021

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Mechanical damages of sugar beet during harvesting affects the quality of the final products and sugar yield. The mechanical damage of sugar beet is assessed randomly by operators of harvesters and can depend on the subjective opinion and experience of the operator due to the complexity of the harvester machines. Thus, the main aim of this study was to determine whether a digital two-dimensional imaging system coupled with convolutional neural network (CNN) techniques could be utilized to detect visible mechanical damage in sugar beet during harvesting in a harvester machine. In this research, various detector models based on the CNN, including You Only Look Once (YOLO) v4, region-based fully convolutional network (R-FCN) and faster regions with convolutional neural network features (Faster R-CNN) were developed. Sugar beet image data during harvesting from a harvester in different farming conditions were used for training and validation of the proposed models. The experimental results showed that the YOLO v4 CSPDarknet53 method was able to detect damage in sugar beet with better performance (recall, precision and F1-score of about 92, 94 and 93%, respectively) and higher speed (around 29 frames per second) compared to the other developed CNNs. By means of a CNN-based vision system, it was possible to automatically detect sugar beet damage within the sugar beet harvester machine.

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“…The average apple detection time was 19ms with FPR at 7.8% and FNR at 9.2% using YOLOv3 and with FPR at 3.5% and FNR at 2.8% using YOLOv5 [12]. Parico and Ahamed proposed a real-time pear fruit counter using YOLOv4 and DeepSORT, with an AP@0.50 of 98% [13]. Yan et al proposed a light-weight fruit target real-time detection method for the apple picking robot based on improved YOLOv5, with an mAP of 86.75% [14].…”

Section: Introductionmentioning

confidence: 99%

Green Citrus Detection and Counting in Orchards Based on YOLOv5-CS and AI Edge System

Lyu

Zhao

et al. 2022

Sensors

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Green citrus detection in citrus orchards provides reliable support for production management chains, such as fruit thinning, sunburn prevention and yield estimation. In this paper, we proposed a lightweight object detection YOLOv5-CS (Citrus Sort) model to realize object detection and the accurate counting of green citrus in the natural environment. First, we employ image rotation codes to improve the generalization ability of the model. Second, in the backbone, a convolutional layer is replaced by a convolutional block attention module, and a detection layer is embedded to improve the detection accuracy of the little citrus. Third, both the loss function CIoU (Complete Intersection over Union) and cosine annealing algorithm are used to get the better training effect of the model. Finally, our model is migrated and deployed to the AI (Artificial Intelligence) edge system. Furthermore, we apply the scene segmentation method using the “virtual region” to achieve accurate counting of the green citrus, thereby forming an embedded system of green citrus counting by edge computing. The results show that the mAP@.5 of the YOLOv5-CS model for green citrus was 98.23%, and the recall is 97.66%. The inference speed of YOLOv5-CS detecting a picture on the server is 0.017 s, and the inference speed on Nvidia Jetson Xavier NX is 0.037 s. The detection and counting frame rate of the AI edge system-side counting system is 28 FPS, which meets the counting requirements of green citrus.

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Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT

Cited by 128 publications

References 41 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

Sugar Beet Damage Detection during Harvesting Using Different Convolutional Neural Network Models

Green Citrus Detection and Counting in Orchards Based on YOLOv5-CS and AI Edge System

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