Comparing bags of features, conventional convolutional neural network and AlexNet for fruit recognition

Hamid, Nik Noor Akmal Abdul; Razali, Rabiatul Adawiya; İbrahim, Zarina Bibi

doi:10.11591/ijeecs.v14.i1.pp333-339

Cited by 13 publications

(5 citation statements)

References 12 publications

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“…The three fully-connected layers, each hosting 4096 neurons, play a vital role in object categorization, while the final SoftMax layer predicts object classes in the images. This architectural design, coupled with specified filter sizes, has become a cornerstone in the field of deep learning [41,42]. The entire course of this study is depicted in Figure 5.…”

Section: Classification Methodsmentioning

confidence: 99%

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Comparative Analysis of Classical Machine Learning and Deep Learning Methods for Fruit Image Recognition and Classification

Salim,

Mohammed

2024

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Section: Classification Methodsmentioning

confidence: 99%

“…We acquired the trained models after the training procedure was finished. The trained model was then used to classify the test dataset [42].…”

Section: Classification Methodsmentioning

confidence: 99%

Comparative Analysis of Classical Machine Learning and Deep Learning Methods for Fruit Image Recognition and Classification

Salim,

Mohammed

2024

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“…It mainly scans this input image. It then generates k output boxes, all with two scores representing probability for an object's availability [16], [17]. Figure 1 depicts the F-RCNN architecture.…”

Section: Architecture Of F-rcnnmentioning

confidence: 99%

Novel deep learning model for vehicle and pothole detection

Gayathri

Thangavelu

2021

IJEECS

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The most important aspect of automatic driving and traffic surveillance is vehicle detection. In addition, poor road conditions caused by potholes are the cause of traffic accidents and vehicle damage. The proposed work uses deep learning models. The proposed method can detect vehicles and potholes using images. The faster region-based convolutional neural network (CNN) and the inception network V2 model are used to implement the model. The proposed work compares the performance, accuracy numbers, detection time, and advantages and disadvantages of the faster region-based convolution neural network (Faster R-CNN) with single shot detector (SSD) and you only look once (YOLO) algorithms. The proposed method shows good progress than the existing methods such as SSD and YOLO. The measure of performance evaluation is Accuracy. The proposed method shows an improvement of 5% once compared with the previous methods such as SSD and YOLO.

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“…The article [14] presented a comparative study between Bag Of Features (BOF), Conventional Convolutional Neural Network (CNN), and AlexNet for fruit recognition. The results indicated that all three techniques had excellent recognition accuracy, but the CNN technique was the fastest at presenting a recognition prediction.…”

Section: Related Workmentioning

confidence: 99%

A Vision-Based Method Utilizing Deep Convolutional Neural Networks for Fruit Variety Classification in Uncertainty Conditions of Retail Sales

Rudnik

Michalski

2019

Applied Sciences

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This study proposes a double-track method for the classification of fruit varieties for application in retail sales. The method uses two nine-layer Convolutional Neural Networks (CNNs) with the same architecture, but different weight matrices. The first network classifies fruits according to images of fruits with a background, and the second network classifies based on images with the ROI (Region Of Interest, a single fruit). The results are aggregated with the proposed values of weights (importance). Consequently, the method returns the predicted class membership with the Certainty Factor (CF). The use of the certainty factor associated with prediction results from the original images and cropped ROIs is the main contribution of this paper. It has been shown that CFs indicate the correctness of the classification result and represent a more reliable measure compared to the probabilities on the CNN outputs. The method is tested with a dataset containing images of six apple varieties. The overall image classification accuracy for this testing dataset is excellent (99.78%). In conclusion, the proposed method is highly successful at recognizing unambiguous, ambiguous, and uncertain classifications, and it can be used in a vision-based sales systems in uncertain conditions and unplanned situations.

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Comparing bags of features, conventional convolutional neural network and AlexNet for fruit recognition

Cited by 13 publications

References 12 publications

Comparative Analysis of Classical Machine Learning and Deep Learning Methods for Fruit Image Recognition and Classification

Comparative Analysis of Classical Machine Learning and Deep Learning Methods for Fruit Image Recognition and Classification

Novel deep learning model for vehicle and pothole detection

A Vision-Based Method Utilizing Deep Convolutional Neural Networks for Fruit Variety Classification in Uncertainty Conditions of Retail Sales

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