Original: Dual phase convolutional neural network based system aimed at small rice grain dataset for disease identification
Revised: Dual phase convolutional neural network based system aimed at small rice grain dataset for disease identification

Original: tahmed bscse uiu ac bd rafeed@cse.uiu.ac bd mabid143108@bscse uiu.ac.bd

Original: Abstract
Revised: Abstract

Original: Although Convolutional Neural Networks (CNNs) are widely used for plant disease detection, they require a large number of training samples while dealing with wide variety of heterogeneous background.
Revised: Although Convolutional Neural Networks (CNNs) are widely used for plant disease detection, they require a large number of training samples while dealing with wide variety of heterogeneous background.

Original: In this paper, a CNN based dual phase method has been proposed which can work effectively on small rice grain disease dataset with heterogeneity.
Revised: In this paper, a CNN based dual phase method has been proposed which can work effectively on small rice grain disease dataset with heterogeneity.

Original: At the first phase, Faster RCNN method is applied for crop- ping out the significant portion (rice grain) from an image.
Revised: At the first phase, Faster RCNN method is applied for cropping out the significant portion (rice grain) from an image.

Original: This initial phase results in a secondary dataset of rice grains devoid of heterogeneous background.
Revised: This initial phase results in a secondary dataset of rice grains devoid of heterogeneous background.

Original: Disease classification is performed on such derived and simplified samples using CNN architectures.
Revised: Disease classification is performed on such derived and simplified samples using CNN architectures.

Original: Comparison of the dual phase approach with straight forward application of CNN  on the small grain dataset shows the effectiveness of the proposed method which provides a 5 fold cross validation accuracy of 88.11%.
Revised: Comparison of the dual phase approach with straight forward application of CNN or Faster RCNN on the small grain dataset shows the effectiveness of the proposed method which provides a five fold cross validation accuracy of 88.11%.

Original: Introduction
Revised: Introduction

Original: As rice grain diseases occur at the very last moment ahead of harvesting, it does major damage to the cultivation process.
Revised: As rice grain diseases occur at the very last moment ahead of harvesting, it does major damage to the cultivation process.

Original: The average loss of rice due to grain discolouration (Baite et al. 2019) was 18.9% in India.
Revised: The average loss of rice due to grain discolouration (Baite et al. 2019) was 18.9% in India.

Original: In Bangladesh False Smut was one of the most destructive rice grain disease (Nessa 2017) from year 2000 to 2017.
Revised: In Bangladesh False Smut was one of the most destructive rice grain disease (Nessa 2017) from year 2000 to 2017.

Original: Collecting field level data on agronomy is a challenging task in the context of poor and developing countries.
Revised: Collecting field level data on agronomy is a challenging task in the context of poor and developing countries.

Original: The challenges include lack of equipment and specialists.
Revised: The challenges include lack of equipment and specialists.

Original: Farmers of such areas are ignorant of technology use which makes it quite difficult to collect crop disease related data efficiently using smart devices via the farmers.
Revised: Farmers of such areas are ignorant of technology use which makes it quite difficult to collect crop disease related data efficiently using smart devices via the farmers.

Original: Hence, scarcity of plant disease oriented data is a common challenge while automating disease detection in such areas.
Revised: Hence, scarcity of plant disease oriented data is a common challenge while automating disease detection in such areas.

Original: Many researches have been undertaken with a view to automating plant disease detection from the very beginning of deep learning revolution utilizing different techniques of machine learning and image processing.
Revised: Many researches have been undertaken with a view to automating plant disease detection from the very beginning of deep learning revolution.

Original: They applied a threshold based clustering algorithm for this task. A framework have been created (Sethy, Negi, and Bhoi 2017) for the detection of defected diseased leaf using K-Means clustering based segmentation.
Revised: Sethy, Negi and Bhoi (2017) applied a threshold based clustering algorithm for this task where they detected defected diseased leaf using K-Means clustering based segmentation.

Original: They claimed that their approach was able to detect the healthy leaf area and defected diseased area accurately.

Original: A genetic algorithm has been devel-
Revised: A genetic algorithm was developed by Chung et al.

Revised: (2016) which was used for selecting essential traits and optimal model parameters for the SVM

Original: [Figure]
Revised: [Figure]

Original: et al. 2016) which was used for selecting essential traits and optimal model parameters for the SVM classi fiers for Bakanae gibberella fujikuroi disease.
Revised: classifiers for Bakanae gibberella fujikuroi disease.

Original: A technique to classify the diseases based on percentage of RGB value of the affected portion was proposed (Islam et al. 2018) utilizing image processing.
Revised: A technique to classify the diseases based on percentage of RGB value of the affected portion was proposed by Islam et al. (2018) utilizing image processing.

Original: A similar technique using multi-level colour image thresholding was proposed (Bakar et al. 2018) for RLB disease detection.
Revised: A similar technique using multi-level colour image thresholding was proposed by Bakar et al. (2018) for RLB disease detection.

Original: Deep learning based object classification and segmentation has become the state-of-the-art for automatic plant disease detection.
Revised: Deep learning based object classification and segmentation has become the state-of-the-art for automatic plant disease detection.

Original: Researchers also experimented with AlexNet (Atole and Park 2018) to distinguish among 3 classes of rice disease using a small dataset containing 227 images.
Revised: Researchers also experimented with AlexNet (Atole and Park 2018) to distinguish among three classes of rice disease using a small dataset containing 227 images.

Original: A similar research for classifying 10 classes of rice disease on aimage dataset was undertaken (Lu et al. 2017) using a handmade deep CNN architecture.
Revised: A similar research for classifying 10 classes of rice disease on aimage dataset was undertaken by Lu et al. (2017) using a handmade deep CNN architecture.

Original: Furthermore, the benefit of us- ing pre-trained model of AlexNet and GoogleNet(Brahimi, Boukhalfa, and Moussaoui 2017) has been demonstrated  when the training data is not large.
Revised: Furthermore, the benefit of using pre-trained model of AlexNet and GoogleNet has been demonstrated by Brahimi, Boukhalfa, and Moussaoui (2017) when the training data is not large.

Original: Their dataset consisted of 9 diseases of tomatoes.
Revised: Their dataset consisted of nine tomato diseases.

Original: A detailed comparative analysis of different state-of-the-art CNN baseline and finely tuned architecture (Rahman et al.  on eight classes of rice disease and pest also conveys a huge potential.
Revised: A detailed comparative analysis of different state-of-the-art CNN baselines and fine tuned architectures (Rahman et al. 2018) on eight classes of rice disease and pest also conveys a huge potential.

Original: It demonstrates two-stage training approach for memory efficient small CNN architectures.
Revised: It demonstrates two-stage training approach for memory efficient small CNN architectures.

Original: Besides works on rice disease some experimental procedure on other agricultural elements are, (Ferentinos 2018) specialized deep learning models based on specific CNN architectures for identification of plant leaf diseases with a dataset containing 58 classes from 25 different plants have been developed. Transfer learning approach using GoogleNet (Barbedo 2018) on a dataset containing 87848 images of 56 diseases infecting 12 plants.
Revised: Faster RCNN has been applied by Bari et al.

Original: Faster RCNN has been applied (Bari et al. 2021) in a realtime approach to diagnose rice leaf disease, despite the dataset  not being natural scene images.
Revised: (2021) on a real-time approach to diagnose rice leaf disease, although this dataset did not contain natural scene images.

Original: Though the above mentioned researches have a significant contribution to the automation of disease detection, none of the works addressed the problem of scarcity of (natural scene) data which limits the performance of CNN based architectures for creating a productive solution.
Revised: Though the above mentioned researches have a significant contribution towards the automation of disease detection, none of the works addressed the problem of scarcity of (natural scene) data which limits the performance of CNN based architectures for creating a productive solution.

Original: Most of the researches focused on image augmentation techniques to tackle the dataset size issue.
Revised: Most of the researches focused on image augmentation techniques to tackle the dataset size issue.

Original: But applying different geometric augmentations on small size images (Liu and Gillies 2016; Shorten and Khoshgoftaar 2019) result in nearly the same type of image production which has drawbacks in terms of neural network training.
Revised: But applying different geometric augmentations on small size images (Liu and Gillies 2016; Shorten and Khoshgoftaar 2019) result in nearly the same type of image production which has drawbacks in terms of neural network training.

Original: Production of similar images through augmentation (Cogswell et al. 2015) can cause overfitting as well.
Revised: Production of similar images through augmentation (Cogswell et al. 2015) can cause overfitting as well.

Revised: Our proposed method consists of two phases.

Original: The first phase of the proposed method deals with a learning oriented segmentation based architecture.
Revised: The first phase of our proposed method deals with a learning oriented localization architecture.

Original: This architecture helps in detecting the significant grain portion of a given image that has a heterogeneous background, which is an easier task compared to disease localization.
Revised: This architecture helps in detecting the significant grain portion of a given image that has a heterogeneous background, which is an easier task compared to disease localization.

Original: The detected grain portions cropped from the original image are used as separate simplified images.
Revised: The detected grain portions cropped from the original image are used as separate simplified images.

Original: In the second phase, these simplistic grain images are used in order to detect grain disease using fine tuned CNN architecture.
Revised: In the second phase, these simplistic grain images are used in order to detect grain disease using fine tuned CNN architecture.

Original: Because of the simplicity of the tasks assigned in the two phases, our proposed method performs well in spite of having only 200 images of three classes.
Revised: Because of the simplicity of the tasks assigned in the two phases, our proposed method performs well in spite of having only 200 images of three classes.

Original: To prove the proposed approach is satisfactory for a small dataset, counter experimentations on straightforward CNN and Faster RCNN were also demonstrated in Appendix D .

Original: Our Dataset
Revised: Our Dataset

Original: Ourbalanced dataset  of 200 images consists of three classes - False Smut, Neck Blast and healthy grain class as shown in Table 1.
Revised: Our dataset (balanced) of 200 images consists of three classes - False Smut, Neck Blast and healthy grain class.

Original:  A sample image  from each class has been shown in Fig.Neck Blast is generally caused by the fungus known as Magnaporthe oryzae .
Revised: Some of these images contain both diseases together. A sample image (with heterogeneous background) from each class has been shown in Fig.

Original: It causes plants to develop very few or no grains at all. Infected nodes result (Wilson and Talbot 2009) in panicle break down. False Smut is caused by the fungus called Ustilaginoidea virens .
Revised: Data have been obtained and annotated from two different sources: (i) image data from a repository (Rahman et al. 2018) that has undergone previous testing and (ii) field data collected under the supervision of staff from the Bangladesh Rice Research Institute (BRRI)

Original: It results in lower grain weight and reduction (Koiso et al. 1994) of seed germination.
Revised: (disease and data collection details in Appendix A ).

Original: [Table]
Revised: [Table]

Original: For this experiment, data has been obtained and annotated from two different sources: image data from a repository (Rahman et al. 2018) that has undergone previous testing and field data collected under the supervision of staff from the Bangladesh Rice Research Institute (BRRI). The
Revised: Table 1 shows detail information about our used data.

Original: Boro rice plant has been chosen for experimental data collection because Boro species pose the greatest risk (Miah et al. 1985) of being negatively impacted by Neck Blast and False Smut.
Revised: We consider multi-class images in our dataset (see Fig.of Appendix A ).

Original: When taking pictures, factors like light, distance, and uniqueness were taken into account. Heterogeneity of the background was the primary factor that was considered.
Revised: In our dual phase approach, a localization algorithm localizes the significant grain portions in phase one.

Original: Some sample images of hetergenous background of the dataset have been presented in Fig. Appendix A provides additional explanations of the dataset.
Revised: Our secondary dataset comes from this phase - original image broken down into multiple sub-images consisting of significant grain portions.

Original: Proposed Dual Phase Approach
Revised: This happens especially in multiclass images (see Fig. 1). No augmentation technique was applied on the training images as these techniques can be prone to overfitting.

Original: In this study, a dual phase approach has been used to learn efficiently from a small dataset of images with significant background heterogeneity.
Revised: Supplementary public data related to the paper can be found at https://zenodo.org/record/7582108 . Proposed Dual Phase Approach

Original: Fig.
Revised: Fig.

Original: 2 shows an overview of the approach.
Revised: 1 shows an overview of our proposed dual phase approach which can learn efficiently from a small dataset of images with significant background heterogeneity.

Original: The initial stage involves taking the original image, cropping it to a specific size, and then running it through a segmentation-focused Faster RCNN architecture.
Revised: The first stage involves taking the original image, cropping it to a specific size, and then running it through a localization-focused Faster RCNN architecture.

Original: From the first phase, two best regions have been chosen at most.
Revised: From the first stage, two most significant regions have been chosen by the algorithm (see

Original: The regions are cropped and resized to a fixed size after the significant grain portions ofan image have been extracted. Because the backgrounds in these images are homogeneous, they appear straightforward.
Revised: Fig. 1). These regions are cropped and resized to a fixed size. The background of these significant region sub-images are less heterogeneous compared to the original full image.

Original: This condensed dataset has been used totrain the CNN architecture for  disease detection. Through experiments, the effectiveness of the learning process has been demonstrated.
Revised: These straightforward significant region sub-images are passed on to CNN model for classification into healthy or a particular disease class.

Original: Segmenting Grain Portion
Revised: Stage 1: Localizing Grain Portion

Original: This is the first stage of our approach.

Original: Images must be a fixed size to be used with segmentation algorithms that use CNN architecture as the backbone. Before feeding input images to the Faster RCNN, they have been scaled down to 640 × 480 .
Revised: Each input image is resized to 640 × 480 and is passed through CNN backbone for feature extraction which in turn is passed on to region proposal network (RPN) for generating region proposals.

Original: Network’s sequential stages through  which this resized image travels are described in Appendix B .
Revised: These proposed regions are passed through ROI pooling layer for getting them to fixed size. Finally, RCNN layer decides which of these proposed regions are significant (details in Appendix B ). The model of this phase only has to predict the significant portions of each image (labeled accordingly). It does not have to worry about predicting class.

Original:  Disease Detection from Segmented Grain
Revised: Stage 2: Disease Detection from Localized Grain

Original: The Faster RCNN architecture is shown drawing bounding boxes on two significant grain portions in Fig.
Revised: The Faster RCNN architecture is shown drawing bounding boxes on two significant grain portions in Fig.

Original: Each of these frames is cropped and resized toa fixed size (in this case, 300 × 250) before being passed through a CNN architecture.
Revised: Each of these frames is cropped and resized to 300 × 250 before being passed through a CNN architecture.

Original: As a result, the single image from the primary dataset has been divided into no more than two images.
Revised: As a result, the single image from the primary dataset has been divided into no more than two images.

Original: Each image in the primary dataset go through the same procedure.
Revised: Each image in the primary dataset goes through the same procedure.

Original: This allows for the creation of a secondary dataset [Figure] [Figure] [Table]
Revised: This allows for the creation of a secondary dataset with significant grain portions (details in Appendix A ).

Original: Total   with significant grain portion.

Original: To train the CNN architecture, each of these images must be assigned to one of the three classes.
Revised: To train the CNN architecture, each of these images must be assigned to one of the three classes.

Original: The full dataset, which includes these secondary image counts, is displayed in Table 2.

Original: As a multiclass data example, the cropped regions in Fig. 2 were predicted by a trained CNN model to represent False Smut and Neck Blast class.
Revised: As a multi-class data example, the cropped regions in Fig. 1 were predicted by a trained CNN model to represent False Smut and Neck Blast class.

Original: Experimental Setup
Revised: Experimental Setup

Original: Different hyperparameter optimization techniques have been covered in this section along with their effects on the experiment.
Revised: We start this section by presenting our algorithm hyperparameters (see Table 5 in Appendix D for a better view).

Original: The hardware used in the experiment, a description of the five basenets used, and evaluation metrics are all described in the Appendix C along with the experimental setup. Optimized Hyperparameters Hyperparameters of Faster RCNN have been presented in
Revised: CNN Backbone: ImageNet Pretrained VGG16 model has been used as CNN backbone for feature extraction.

Original: Table 5 in Appendix C .
Revised: [Figure]

Original: Anchor Box Hyperparameters: Anchor boxes are a set of bounding boxes defined through different scales and aspect ratios. They mark the probable regions of interest of different shapes and sizes.
Revised: Anchor Box Hyperparameters: We use four different size anchor boxes (32, 64, 128, 256 pixels) with four different ratios ((1, 1), ( 1 √ 2 , 2 √ 2 ), ( 2 √ 2 , 1 √ 2 ), (2, 2) ) for each box.

Original: The total number of probable anchor boxes per pixel of a convolutional feature map is P n × R n , where P n and R n denote the number of anchor box size variations and ratio variations respectively.
Revised: So, the algorithm can propose at most 16 ( 4 × 4 ) anchor boxes per pixel.

Original: Region Proposal Network (RPN) Hyperparameters:
Revised: Region Proposal Network (RPN) Hyperparameters:

Original: RPN layer utilizes the convolutional feature map of the original image to propose regions of interest that are identifiable within the original image.

Original: The proposals are made in line with the anchor boxes.

Original: For each anchor box, RPN predicts if it is an object of interest or not and changes the size of the anchor box to better fit the object.

Original: RPN threshold of 0.4 - 0.8 means that any proposed region which has IoU (Intersection Over Union) less than 0.4 with  ground truth object is considered a wrong guess  whereas any proposed region which has IoU greater than 0.8 with  ground truth object is considered correct.
Revised: Any proposed region with IoU (Intersection Over Union) less than 0.4 with a ground truth object is regarded as an incorrect guess due to the RPN threshold of 0.4 - 0.8, whereas any proposed region with IoU greater than 0.8 with a ground truth object is considered correct.

Original: This notion is used for training the RPN layer.
Revised: This notion is used for training the RPN layer.

Original: Proposal Selection: Proposal selection threshold ofmeans that top (according to probability) 200 region proposals from  RPN layer will pass on to the next layers for further processing. Overlap Threshold:
Revised: Topregion proposals from the RPN layer is passed on to the following layers.

Original: During non-max suppression, overlapping object proposals are excluded ifthe IoU is above a certain threshold.
Revised: During non-max suppression, overlapping object proposals are excluded if IoU > 0 . 8 .

Original: If their overlap is greater than the threshold, only the proposal with the highest probability is kept and the procedure continues until there are no more boxes with sufficient overlap.

Original: Learning Rate: It is used for controlling the speed of model parameter update. Optimizer: Optimizer is an algorithm for updating model parameter weights after training on each batch of samples. Weight updating process varies with the choice of optimizer.
Revised: Learning Rate and Optimizer: Adam optimizer with a learning rate of 0.0001 is used during model training.

Original: As the proposed method has two stages: segmentation and classification, failure in proper segmentation can leads to classification failure. In this work, two stages are created as an intact pipeline so that outcome of the first stage directed to thesecond stage as input.
Revised: We use mAP score (mean average precision) for looking at the phase one localization algorithm performance, while we use accuracy for the stage two classification model.

Original: Detail about the procedure mentioned in Subsection Prime Experiment.
Revised: Details of mAP and hardware used for training can be found in Appendix C .

Original: Results and Discussion
Revised: Results and Discussion

Original: The proposed dual-phase approach has been carried out in two steps, as it was previously mentioned.
Revised: The proposed dual-phase approach has been referred to as the prime experiment .

Original: First, the grain parts are segmented, and then the segmented parts are classified. Throughout the paper, this experiment has been referred to as the prime experiment. Different CNN architectures and the Faster RCNN have each been used separately to test the performance of the prime experiment. This part of the experiment has been mentioned asthe counter experiment described in Appendix D .
Revised: Straightforward end-to-end classification of disease using CNN has been described as counter experiment 01 , while using Faster RCNN/ YOLO to directly localize and classify significant portions of each image in a single stage has been referred to as counter experiment 02 .

Original: Prime Experiment: Dual Phase Approach
Revised: Prime Experiment: Dual Phase Approach

Original: Prime experiment has been performed by creating a pipeline in two phases  shown in Fig.
Revised: Prime experiment has been performed by creating a pipeline of two phases as shown in Fig.

Revised: Five fold cross-validation has been performed for hyperparameter tuning.

Original: In the first phase, segmentations of grain has been performed and the segmented parts have been cropped and saved as the secondary dataset, mentioned in Subsection Disease Detection from Segmented
Revised: Phase One: Localization of Grains Extracting the significant portion (grain) from a specific image is the goal of phase one. Three different CNN architectures (VGG16, [Figure] VGG19, and ResNet50) have been tested as the backbone of

Revised: Faster RCNN.

Revised: Details of the hyperparameter setting derivation experiments of Faster RCNN have been provided in

Revised: Faster RCNN Based Localization Experiments subsection of

Revised: Appendix D .

Revised: The goal of these experiments is to choose hyperparameters such that mAP score for significant grain portion localization is maximized.

Revised: Table 2 shows the five fold cross-validation mAP scores for the different CNN backbones, while utilizing the chosen hyperparameters.

Revised: Faster

Revised: RCNN with VGG16 as backbone achieved the best mAP score of 76 .

Revised: 32 ± 2 . 29 .

Revised: Some sample outcomes from phase one are shown in Fig.

Revised: Phase Two: Classification Phase two is where the classification result is produced using the image data from phase one.

Revised: In this phase, three different CNN architectures VGG16, VGG19, and ResNet50 have once more been used for comparison.

Revised: The best hyperparameter settings from counter experiment 01 (experiment details in Appendix D ) has been reapplied in this phase.

Revised: With a validation accuracy of 88 .

Revised: 11 ± 3 . 86 , VGG16 stood out as having the best performance as mentioned in Table 3.

Original: AnchorBoxRatio
Revised: AnchorBoxRatio

Original: AnchorBoxPixels
Revised: AnchorBoxPixels

Original: RPNThreshold
Revised: RPNThreshold

Original: CNNArchitecture
Revised: CNNArchitecture

Original: OverlapThreshold
Revised: OverlapThreshold

Original: [Table]
Revised: [Table]

Original: (1:1),( 1 √ 2 : 2 √ 2 ), ( 2 √ 2 : 1 √ 2 ),(2:2))

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Figure] [Table] [Figure]
Revised: The common approach for rice disease classification from a given image would be to simply pass the image through trained CNN model and perform end-to-end classification.

Original: [Table] [Figure] [Table] [Figure]
Revised: This is what we do in counter experiment 01 (details provided in Appendix D ).

Original: [Table] [Figure] Grain. K fold cross-validation (K=5) have been performed where train and validation split was 80:20.
Revised: We experiment with five different

Original: As a result, the full primary dataset has been converted into a secondary dataset. After labeling the secondary dataset into three classes, three chosen CNN architectures have been used in second phase to perform the final classification.
Revised: CNN architectures using transfer learning, fine tuning and adding regularization schemes such as dropout. The best five fold cross-validation accuracy that we could achieve was an accuracy of around 69% using regularized and fine-tuned

Original: Phase One: Segmentation of Grains To remove the significant portion (grain) from a specific image is the goal of phase one. Three different CNN architectures have been used as the backbone, with Faster RCNN being used in between. VGG16, VGG19, and ResNet50 CNN architectures were used, which have also been used in counter experiment 02, mentioned in Subsection Counter Experiment 02.
Revised: VGG16 model (see Table 6 of Appendix D ) which is significantly lower than our proposed dual phase approach performance (around 88% accuracy). It is to note that CNN models with softmax layer at the output are not capable of detecting multiple diseases simultaneously in a single image.

Original: Also, hyperparameters setting have been followed from the counter experiment 02. Only the best performed hyperparameter setting were applied in phase onementioned in Table 5. Faster RCNN with VGG16 as backbone achieved the best mAP score of 84 .
Revised: In order to simplify things, we consider CNN prediction to be correct for a multi-class image if any one of the present diseases is identified by the model. Counter Experiment

Original: 3 ± 2 . 36 . The outcome has been obtained using5-fold cross validation. As a result, every image in the dataset has been assessed. At most two new images could be generated from each original image, resulting in the creation of a secondary dataset.
Revised: One can argue that instead of using the dual phase approach, we can use a localization algorithm to directly extract and classify the significant grain portions of a given image.

Original: Two of each image’s best bounding boxes have been chosen to perform this operation. The first bounding box, which is cropped and included in the new dataset, is the best bounding box outlined by the Faster RCNN. Second bounding boxes that meet the accuracy threshold of 90% and IoU threshold of 0.5 have been chosen.
Revised: In that way, we only need a single phase. If a particular disease exists in the image and if one of the extracted sub-images is labeled as that particular disease by the localization algorithm (other extracted portions can be labeled as healthy, but none of them can be labeled as some other disease), then we can consider a correct classification.

Original: Multiple images lacked bounding boxes that concurred with this requirement. In that instance, only one image has been chosen for the new dataset.
Revised: In case of the existence of multiple diseases in the image, both diseases need to be labeled in at least one of the extracted sub-images (one sub-image per disease). Comparative performance between

Original: The bounding boxes from each phase one image are shown in Fig.
Revised: Faster RCNN, YOLO v5, and the proposed dual phase approach has been shown in Table 4.

Original: Only one bounding box is detected in this instance on subfigure (c).
Revised: We can see that the performance of YOLO v5 is even worse than our best end-toend CNN model (68% vs 69% accuracy, see previous section), while our proposed approach achieves over 88% validation accuracy.

Original: Phase Two: Classification Phase two is where the classification result is produced using the image data from phase one. In this phase, three different CNN architectures VGG16, VGG19, and ResNet50 have once more been used.
Revised: A large amount of training data is necessary for localization algorithms like Faster RCNN and YOLO to converge during training, especially when they have to perform classification besides localization.

Original: In the phase referred to Appendix D , the best settings from counter experiment 01 have been reapplied. With a validation accuracy of 88 .
Revised: Faster RCNN and

Original: 11 ± 3 . 86 , VGG16 stood out as having the best performance mentioned in Table 4.
Revised: YOLO both failed to outperform the proposed approach because of the current setup’s insufficient data count.

Original: Conclusion and Future Work
Revised: Conclusion and Future Work

Original: In a nutshell, the following contributions are made by this research: A dual phase strategy that can learn from a small dataset on rice grain diseases has been put forth.
Revised: The main motivation behind this project is to identify a solution for limited labeled agriculture data (supervised task). We offer a solution that can perform well in spite of having a small dataset in a multi-class classification context with heterogeneous image background.

Original: Phase one has proposed a smart segmentation method that can handle the heterogeneous background present in the dataset of plant disease images, that was gathered in a real-world setting.
Revised: Phase one provides a smart localization method that can handle the heterogeneous background present in the real world data of plant disease images.

Revised: The goal of this first phase is to make the classification task of phase two easier.

Original: On a small dataset of rice grain images, an experimental comparison with the use of current CNN architectures has been offered to demonstrate the efficacy of the suggested method.
Revised: On a small dataset of rice grain images, an experimental comparison with the use of current CNN architectures has been offered to demonstrate the efficacy of the suggested method.

Original: Since there are only 3 classes and they differ greatly in terms of class features, expectancy from CNN models like
Revised: Our proposed approach is model agnostic - one can use any localization algorithm in phase one and any CNN classification model in phase two. Experiments looking at the combination of different architectures in these two phases can improve performance further.

Original: VGG16 is higher in this dataset. This process is pipelinebased, and phase one may produce FP/FN results that are passed on to phase two.
Revised: Our proposed process is pipeline-based, and phase one may produce false positive or false negative results that are passed on to phase two.

Original: Phase 2 will not be able to properly classify them as a result.
Revised: Phase two will not be able to properly classify them in such cases.

Original: That is one drawback of this system.
Revised: This is one drawback of this system and should be looked into in future.

Original: This problem can be resolved by introducing a new class called ”No Grain” that will declare everything except grain.

Original: Appendix A. Additional Informations on Dataset
Revised: Appendix A. Additional Information on Dataset

Original: To make the dataset more challenging multiclass images also have been taken into account.

Original: Sample images of multiclass data which consists both Neck Blast and False Smut class have been presented in Fig.
Revised: We have two diseases in the dataset - False Smut and Neck Blast. Neck Blast is generally caused by a fungus known as

Original: If there is a multiclass data it had been labelled as Neck Blast for the training phase of counter experiments (explained in Section Results and 
Revised: Magnaporthe oryzae . It causes plants to develop very few or no grains at all. Infected nodes result (Wilson and Talbot 2009) in panicle break down.

Original: Discussion) as False Smut already surpassed byquantity than Neck Blast.
Revised: False Smut is caused by a fungus called Ustilaginoidea virens . It results in lower grain weight and reduction (Koiso et al. 1994) of seed germination. The Boro rice plant has been chosen for experimental data collection, because Boro species falls under the greatest risk (Miah et al. 1985) of being negatively impacted by Neck Blast and False Smut.

Original: Also, the dataset was split into 80:20 in terms of train and validation set. Augmentation techniques were not applied as the main goal of this experiment is to use small and natural scene image data.
Revised: When taking pictures, factors like light, distance, and uniqueness were taken into account. Heterogeneity of the background was the primary factor that was considered.

Original: Additionally, there are other factors that can spoil the experiment, such as illumination, symptom severity, maturity of the plant and diseases.
Revised: Some factors that can spoil the experiment are - illumination, symptom severity, maturity of the plant and diseases.

Original: A large versatile dataset can attend on that occasion which can be achieved in the future.
Revised: A large versatile dataset can attend on such occasions which can be achieved in the future.

Original: The dataset has been kept to three classes for the early stages of the investigation.
Revised: The dataset has been kept to three classes for the early stages of the investigation.

Original: Also, it is quite challenging and burdensome to collect different rice disease image dataset throughout the year as different diseases occur at different time.
Revised: Also, it is quite challenging and burdensome to collect different rice disease image dataset throughout the year as different diseases occur at different time.

Original: So, at this early stage of the investigation three classes is competent to deliver a sufficient result.
Revised: So, at this early stage of the investigation three classes is competent to deliver a sufficient result.

Original: Supplementary public data related to the paper can be found at https://cutt.ly/RvgxoDi.

Original: Appendix B. Consecutive Stages of Phase One
Revised: Appendix B. Consecutive Stages of Phase One

Original: Convolutional Neural Network (CNN):
Revised: Convolutional Neural Network (CNN):

Original: In order to avoid sliding a window in each spatial position of the original image, CNN architecture is used in order to learn and extract feature map from the image which represents the image effectively.
Revised: In order to avoid sliding a window in each spatial position of the original image, CNN architecture is used in order to learn and extract feature map from the image which represents the image effectively.

Original: The spatial dimension of such feature map decreases whereas the channel number increases.
Revised: The spatial dimension of such feature map decreases whereas the channel number increases.

Original: For the dataset used in this research, VGG16 architecture has proven to be the most effective.
Revised: For the dataset used in this research, VGG16 architecture has proven to be the most effective.

Original: Hence, VGG has been used as the backbone CNN architecture which transforms the original image into 20 × 15 × 512 dimension.
Revised: Hence, VGG has been used as the backbone CNN architecture which transforms the original image into 20 × 15 × 512 dimension.

Original: Region Proposal Network (RPN): The extracted feature map is passed through RPN layer.
Revised: Region Proposal Network (RPN): The extracted feature map is passed through RPN layer.

Original: For each pixel of the feature map of spatial size 20 × 15, there are 16 possible bounding boxes (4 different aspect ratios and 4 different sizes mentioned in bold letter in Table 5).
Revised: For each pixel of the feature map of spatial size 20 × 15, there are 16 possible bounding boxes (4 different aspect ratios and 4 different sizes mentioned in bold letter in Table 5).

Original: So, that makes total 16 × 20 × 15 = 4800 possible bounding boxes,
Revised: So, that makes total 16 × 20 × 15 = 4800 possible bounding boxes,

Original: RPN is a two branch Convolution layer which provides two scores (branch one) and four coordinate adjustments (branch two) for each of the 4800 boxes.
Revised: RPN is a two branch Convolution layer which provides two scores (branch one) and four coordinate adjustments (branch two) for each of the 4800 boxes.

Original: The two scores correspond to the probability of being an object and a non-object.
Revised: The two scores correspond to the probability of being an object and a non-object.

Original: Only those boxes which have a high object probability are taken into account.
Revised: Only those boxes which have a high object probability are taken into account.

Original: Non-max suppression (NMS) is used in order to eliminate overlappingobject bounding boxes and to keep only the high probability unique boxes.
Revised: To remove overlapping bounding boxes and retain the high probability unique boxes, non-max suppression (NMS) is used.

Original: The  threshold ofthis overlap in this case is 0.8 IoU. From this probable object proposals, topproposals according to object probability  are passed to the next layers.
Revised: The overlap must meet a threshold of 0.8 IoU. Top 200 proposals ranked by object probability from the object proposals are forwarded to the next layers.

Original: ROI Pooling:
Revised: ROI Pooling:

Original: Each of the 200 selected object proposals correspond to some region in the CNN feature map.
Revised: Each of the 200 selected object proposals correspond to some region in the CNN feature map.

Original: For passing each of these regions on to the dense layers of the architecture, each of the regions need to be of fixed size.
Revised: For passing each of these regions on to the dense layers of the architecture, each of the regions need to be of fixed size.

Original: ROI pooling layer takes each region and turns them into 7 × 7 × 512 using bilinear interpolation and max pooling.
Revised: ROI pooling layer takes each region and turns them into 7 × 7 × 512 using bilinear interpolation and max pooling.

Original: RCNN Layer: RCNN layer consists of fully connected dense layers.
Revised: RCNN Layer: RCNN layer consists of fully connected dense layers.

Original: Each of the 7 × 7 × 512 size feature maps are flattened and passed through these fully connected layers.
Revised: Each of the 7 × 7 × 512 size feature maps are flattened and passed through these fully connected layers.

Original: The final layer has two branches.
Revised: The final layer has two branches.

Original: Branch one predicts if the input feature map is background class or significant grain portion.
Revised: Branch one predicts if the input feature map is background class or significant grain portion.

Original: Branch two provides four regression values denoting the adjustment of the bounding box to better fit the grain portion.
Revised: Branch two provides four regression values denoting the adjustment of the bounding box to better fit the grain portion.

Original: For each feature map, if the probability of being a grain is over 0.6, only then is the feature map considered as a probable grain portion and the adjusted coordinates are mapped to the original image in order to get the localized grain portion.
Revised: For each feature map, if the probability of being a grain is over 0.6, only then is the feature map considered as a probable grain portion and the adjusted coordinates are mapped to the original image in order to get the localized grain portion.

Original: The overlapping boxes are eliminated using NMS.
Revised: The overlapping boxes are eliminated using NMS.

Original: The remaining bounding box regions are the significant grain portions.
Revised: The remaining bounding box regions are the significant grain portions.

Original: Loss Function:
Revised: Loss Function:

Original: The trainable layers of Faster RCNN architecture are: CNN backbone, RPN layer and RCNN layer.
Revised: The trainable layers of Faster RCNN architecture are: CNN backbone, RPN layer and RCNN layer.

Original: A loss function is needed in order to train these layers in an end to manner which is as follows.
Revised: A loss function is needed in order to train these layers in an end to manner which is as follows.

Original: [Equation]
Revised: [Equation]

Original: The first term of this loss function defines the classification loss over two classes which describe whether predicted bounding box i is an object or not.
Revised: The first term of this loss function defines the classification loss over two classes which describe whether predicted bounding box i is an object or not.

Original: The second term defines the regression loss of the bounding box when there is a ground truth object having significant overlap with the box.
Revised: The second term defines the regression loss of the bounding box when there is a ground truth object having significant overlap with the box.

Original: Here, p i and t i denote predicted object probability of bounding box i and predicted four coordinates of that box respectively while p ∗ i and t ∗ i denote the same for the ground truth bounding box which has enough overlap with predicted bounding box i .
Revised: Here, p i and t i denote predicted object probability of bounding box i and predicted four coordinates of that box respectively while p ∗ i and t ∗ i denote the same for the ground truth bounding box which has enough overlap with predicted bounding box i .

Original: N cls is the batch size (256 in this case) and N reg is the total number of bounding boxes having enough overlap with ground truth object.
Revised: N cls is the batch size (256 in this case) and N reg is the total number of bounding boxes having enough overlap with ground truth object.

Original: Both these terms work as normalization factor.
Revised: Both these terms work as normalization factor.

Original: L cls and L reg are log loss (for classification) and regularized loss (for regression) function respectively.
Revised: L cls and L reg are log loss (for classification) and regularized loss (for regression) function, respectively.

Original: Appendix C. Hardware, Utilized CNN Models and Evaluation Metrics
Revised: Appendix C. Hardware, Utilized CNN Models and Evaluation Metrics

Original: For the training environment, assistance has been taken from two different sources.
Revised: For the training environment, assistance has been taken from two different sources.

Original: • Royal Melbourne Institute of Technology (RMIT) provides GPU for international research enthusiasts and they provided a Red Hat Enterprise Linux Server along with the processor Intel Xeon E5-2690 CPU, clock speed of 2.60 GHz.
Revised: • Royal Melbourne Institute of Technology (RMIT) provides GPU for international research enthusiasts and they provided a Red Hat Enterprise Linux Server along with the processor Intel Xeon E5-2690 CPU, clock speed of 2.60 GHz.

Original: It has 56 CPUs with two threads per core,GB of RAM.
Revised: It has 56 CPUs with two threads per core,GB of RAM.

Original: Each user can use up to 1 petabyte of storage.
Revised: Each user can use up to 1 petabyte of storage.

Original: There are also two 16 GB NVIDIA Tesla P100-PCIE GPUs available.
Revised: There are also two 16 GB NVIDIA Tesla P100-PCIE GPUs available.

Original: First phase was completed through this server.
Revised: First phase was completed through this server.

Original: • Google Colab (Tesla K80 GPU, 12GB RAM) and Kaggle kernel (Tesla P100 GPU) have been used for counter experimentation.
Revised: • Google Colab (Tesla K80 GPU, 12GB RAM) and Kaggle kernel (Tesla P100 GPU) have been used for counter experimentation.

Original: Fig.
Revised: Fig.

Original: 5 shows architectures and key blocks of the applied
Revised: 5 shows architectures and key blocks of the applied

Original: CNN architectures.
Revised: CNN architectures.

Original: Experiments have been performed using five state-of-the-art CNN architectures which are described as follows.
Revised: Experiments have been performed using five state-of-the-art CNN architectures which are described as follows.

Original: VGG16 is a sequential architecture (Simonyan and Zisser-
Revised: VGG16 is a sequential architecture (Simonyan and Zisser-

Original: man
Revised: man

Original: consisting of 16 convolutional layers.
Revised: consisting of 16 convolutional layers.

Original: Kernel size in all convolution layers is three.
Revised: Kernel size in all convolution layers is three.

Original: VGG19 has three extra convolutional
Revised: VGG19 has three extra convolutional

Original: layers
Revised: layers

Original: Simonyan and
Revised: Simonyan and

Original: Zisserman 2014)
Revised: Zisserman 2014)

Original: and the rest is the same as VGG
Revised: and the rest is the same as VGG

Original: ResNet50 belongs to the family of residual neural networks.
Revised: ResNet50 belongs to the family of residual neural networks.

Original: It is a deep CNN architecture (He et al. 2016) with skip connections and batch normalization.
Revised: It is a deep CNN architecture (He et al. 2016) with skip connections and batch normalization.

Original: The skip connections help in eliminating the gradient vanishing problem.
Revised: The skip connections help in eliminating the gradient vanishing problem.

Original: InceptionV3 is a CNN architecture (Szegedy et al. 2016) with parallel convolution branching.
Revised: InceptionV3 is a CNN architecture (Szegedy et al. 2016) with parallel convolution branching.

Original: Some of the branches have filter size as large as 7 × 7 .
Revised: Some of the branches have filter size as large as 7 × 7 .

Original: Xception takes the principles of Inception to an extreme.
Revised: Xception takes the principles of Inception to an extreme.

Original: Instead of partitioning the input data into several chunks, it maps the spatial correlations (Chollet 2017) for each output channel separately and performs 1 × 1 depthwise convolution.
Revised: Instead of partitioning the input data into several chunks, it maps the spatial correlations (Chollet 2017) for each output channel separately and performs 1 × 1 depthwise convolution.

Original: All results have been provided in terms of 5 fold cross validation.
Revised: All results have been provided in terms of 5 fold cross validation.

Original: Accuracy metric has been utilized in order to compare dual phase approach against implementation of CNN on original images without any segmentation.
Revised: Accuracy metric has been utilized in order to compare dual phase approach against implementation of CNN on original images without any segmentation.

Original: Accuracy is a suitable metric for balanced dataset.
Revised: Accuracy is a suitable metric for balanced dataset.

Original: [Equation]
Revised: [Equation]

Original: Segmenting the grain portion is the goal of the first phase of the dual phase approach.
Revised: Segmenting the grain portion is the goal of the first phase of the dual phase approach.

Original: For evaluating the performance of this phase, mAP (mean average precision) score has been used.
Revised: For evaluating the performance of this phase, mAP (mean average precision) score has been used.

Original: Precision, recall and IoU (Intersection over Union) are required to calculate mAP score.
Revised: Precision, recall and IoU (Intersection over Union) are required to calculate mAP score.

Original: [Equation]
Revised: [Equation]

Revised: Recall =

Original: [Equation]
Revised: [Equation]

Original: If a predicted box IoU is greater than a certain predefined threshold, it is considered as TP.
Revised: If a predicted box IoU is greater than a certain predefined threshold, it is considered as TP (true positive).

Original: Otherwise, it is considered as FP.
Revised: Otherwise, it is considered as FP (false positive).

Original: ( TP + FN )  is actually the total number of ground truth bounding boxes.
Revised: ( TP + FN ) (FN being false negative) is actually the total number of ground truth bounding boxes.

Original: Average precision (AP) is calculated from the area under the precision-recall curve.
Revised: Average precision (AP) is calculated from the area under the precision-recall curve.

Original: If there are N classes, then mAP is the average AP of all these classes.
Revised: If there are N classes, then mAP is the average AP of all these classes.

Original: In this research, there is only one class of object in phase one, that is the significant grain portion class.
Revised: In this research, there is only one class of object in phase one, that is the significant grain portion class.

Original: So, here AP and mAP are the same.
Revised: So, here AP and mAP are the same.

Original: Appendix D.  Counter Experiments
Revised: Appendix D. Experiment Details Counter Experiment

Original: Individual counter experiments have been performed to analyze their performance with the respective phase of the prime experiment.

Original: Counter Experiments

Original: There have been two counter experiments, counter experiment 01 and counter experiment 02.

Original: The objective of counter experiment 01, which is based on different CNN architectures, is to obtain the classification result from the main dataset.

Original: The second counter experiment, which has been used for both classification and detection of the three classes, is based on a Faster RCNN that underpins three chosen CNN architectures.

Original: Counter Experiment 01: CNN

Original: In this experiment, five different CNN architectures were used.
Revised: In this experiment, five different CNN architectures were used (see Table 6).

Original: In this section, using imagenet pretrained models, three transfer learning methodologies, freezed layer, fine tuning, and fine tuning + dropout have been applied.
Revised: Using imagenet pretrained models, three transfer learning methodologies, frozen layer, fine tuning, and fine tuning + dropout have been applied.

Original: The freezing layer approach, also known as the default transfer learning method, was used initially.
Revised: The freezing layer approach, also known as the default transfer learning method, was used initially.

Original: With a validation accuracy of 63 .
Revised: With a validation accuracy of 63 .

Original: 33 ± 2 . 04 , VGG16 performed better than other CNN architectures.
Revised: 33 ± 2 . 04 , VGG16 performed better than other CNN architectures.

Original: After that, a finely tuned approach has been used, which resulted in an increase in validation accuracy for VGG16 of 67 .
Revised: After that, fine tuning has been used, which resulted in an increase in validation accuracy for VGG16 of 67 .

Original: 79 ± 3 .24 .
Revised: 79 ± 3 .

Original: The CNN architectures have been modified to incorporate dropout, which yields a significant improvement of 69 .
Revised: The CNN architectures have been modified to incorporate dropout, which yields a significant improvement of 69 .

Original: 43 ± 3 .
Revised: 43 ± 3 .

Original: 41 for VGG16.
Revised: 41 for VGG16.

Original: By experimenting with dropout on different positions inside individual CNNs, fine-tuning and fine-tuning + dropout have both been performed repeatedly.
Revised: By experimenting with dropout on different positions inside individual CNNs, finetuning and fine-tuning + dropout have both been performed repeatedly.

Original: Although the VGG16 results in high standard deviation, which is a sign of poor precision. In Table 6, comparative results for each of the five architectures are displayed. Counter Experiment 02:
Revised: Faster RCNN Based Localization Experiments

Original: Faster RCNN Faster RCNN has been employed in this counter experiment 02, with three different CNN architectures serving as the backbone.
Revised: Three different CNN architectures have been tested as the backbone of Faster RCNN.

Original: The purpose of this experiment is to evaluate Faster RCNN’s capability for effective significant portion detectionand classification (grain).
Revised: Purpose of this experiment is to evaluate Faster RCNN’s capability for effective significant portion detection (grain).

Revised: Additionally, ResNet50 was chosen over Xception and InceptionV3 (which are mentioned in Table 6) due to the lower validation loss along with

Original: The effectiveness of VGG16 and VGG
Revised: VGG16 and VGG19 from previous experiment.

Revised: Pretrained models have been used from COCO and Imagenet.

Revised: For

Original: Conv - 1 ConvPooling
Revised: Faster RCNN, various hyperparameter optimizations have been used to achieve best results are shown in Table 7. Default settings from Faster RCNN paper (Ren et al. 2015) for anchor box ratio were (1:1), (2:1), (1:2) and anchor box pixels were 128, 256, 512 which produces 3 × 3= anchor boxes per pixel. The default RPN threshold of (0.30.7), overlap threshold 0.8 and default anchor box ratios and pixels, VGG16 (imagenet pretrained model) provided the best mAP score of 71 . 0 ± 4 . 0 .

Revised: After tuning RPN threshold to (0.4 - 0.8), anchor box ratios to (1:1), ( 1 √ 2 : 2 √ 2 ), ( 2 √ 2 : 1 √ 2 ), (2:2) and pixel sizes to 32, 64, 128, 256, 4 × 4=16 anchor

Original: Conv - 1  ConvConv - 3 Pooling
Revised: Conv - 1 ConvPooling Conv - 1 ConvConv - 3 Pooling

Original: INPUT
Revised: INPUT

Original: Dense Dense
Revised: Dense Dense

Original: Dense
Revised: Dense

Original: X X
Revised: X X

Original: OUTPUT
Revised: OUTPUT

Original: Conv - 1 ConvPooling
Revised: Conv - 1 ConvPooling

Original: Conv - 1 ConvConv -
Revised: Conv - 1 ConvConv -

Original: Pooling
Revised: Pooling

Original: INPUT
Revised: INPUT

Original: Dense Dense
Revised: Dense Dense

Original: Dense
Revised: Dense

Original: X X
Revised: X X

Original: OUTPUT
Revised: OUTPUT

Original: ConvReLU addition weight weight
Revised: ConvReLU addition weight weight

Original: BN
Revised: BN

Original: ReLU
Revised: ReLU

Original: BN
Revised: BN

Original: X l
Revised: X l

Original: X l+
Revised: X l+

Original: PreviousLayer 1X1Conv
Revised: PreviousLayer 1X1Conv

Original: 1X1Conv 3X3MaxPooling 3X3Conv 5X5Conv 1X1Conv
Revised: 1X1Conv 3X3MaxPooling 3X3Conv 5X5Conv 1X1Conv

Original: Filter Concatenation 1X1Conv
Revised: Filter Concatenation 1X1Conv

Original: I N P U T 1X1Conv
Revised: I N P U T 1X1Conv

Original: 3X 3X 3X 3X
Revised: 3X 3X 3X 3X

Original: 3X
Revised: 3X

Original: 3X 3X
Revised: 3X 3X

Original: Concat
Revised: Concat

Original: Output Channels
Revised: Output Channels

Original: [Figure]
Revised: [Figure]

Original: e)
Revised: (e)

Original: Hyperparameter
Revised: Hyperparameter

Original: Optimized Value
Revised: Optimized Value

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table]
Revised: [Table]

Original: 1,2)], [ (1,1), ( 1 √ 2 , 2 √ 2 ), ( 2 √ 2 , 1 √ 2 ), (2,2) ]
Revised: 1,2)], [ (1,1), ( 1 √ 2 , 2 √ 2 ), ( [Table] 2 , 1 √ 2 ), (2,2) ]

Original: RPN Threshold 0.
Revised: RPN Threshold 0.

Original: [Table] [Table]
Revised: [Table] [Table]

Original: Selection 200 ,
Revised: Selection 200 ,

Original: [Table]
Revised: [Table]

Original: Optimizers

Original: [Table][Table] [Table]
Revised: [Table]

Original: has been determined by counter experiment 01.

Original: Additionally, ResNet50 was chosen over Xception and InceptionV (which are mentioned in Table 6) due to the lower validation loss.

Original: CNN models that were pretrained using data from COCO and Imagenet have been used.

Original: For the Faster

Original: RCNN model, various hyperparameter optimizations have been used to achieve the best results, which are shown in

Original: Table 7.
Revised: [Table]

Original: Default settings from the Faster RCNN paper (Ren et al.

Original: 2015) for anchor box ratio were (1:1), (2:1), (1:2) and anchor box pixels were 128, 256, 512. Which produces 3 × 3=9 anchor boxes per pixel. The default RPN threshold of (0.30.7), overlap threshold 0.8 and default anchor box ratios and pixels, VGG16 (imagenet pretrained model) provided the best mAP score of 71 . 0 ± 4 . 0 . After tuning RPN threshold to (0.4 - 0.8), anchor box ratios to (1:1), ( 1 √ 2 : 2 √ 2 ), ( 2 √ 2 : 1 √ 2 ), (2:2) and pixel sizes to 32, 64, 128, 256, 4 × 4=16 anchor boxes have been produced which provides better outcome than before.
Revised: boxes have been produced which provides better outcome than before.

Original: This setting improved the mAP for VGG16 (imagent pretrained model) to 76 .
Revised: This setting improved the mAP for VGG16 (imagent pretrained model) to 76 .

Original: 32 ± 2 . 29 which is the peak outcome after several optimization.
Revised: 32 ± 2 . 29 which is the peak outcome after several optimization.

Original: PretrainedModel
Revised: PretrainedModel

Original: AnchorBoxRatio
Revised: AnchorBoxRatio

Original: AnchorBoxPixels
Revised: AnchorBoxPixels

Original: RPNThreshold
Revised: RPNThreshold

Original: CNNArchitecture
Revised: CNNArchitecture

Original: OverlapThreshold
Revised: OverlapThreshold

Original: [Table]
Revised: [Table]

Original: Imagenet (1:1), (2:1), (1:2) 128,256,
Revised: Imagenet (1:1), (2:1), (1:2) 128,256,

Revised: [Table]

Revised: [Table]

Original: [Table]
Revised: [Table]

Original: (1:1), ( 1 √ 2 : 2 √ 2 ), ( 2 √ 2 : 1 √ 2 ), (2:2) 32, 64,
Revised: (1:1), ( 1 √ 2 : 2 √ 2 ), ( 2 √ 2 : 1 √ 2 ), (2:2) 32, 64,

Original: [Table]
Revised: [Table]

Original: [Table]

Original: [Table]
Revised: [Table]

Original: COCO (1:1), (2:1), (1:2) 128,256,
Revised: COCO (1:1), (2:1), (1:2) 128,256,

Original: [Table]
Revised: [Table]

Original: [Table]
Revised: [Table]

Original: (1:1), ( 1 √ 2 : 2 √ 2 ), ( 2 √ 2 : 1 √ 2 ), (2:2) 32, 64, 128,
Revised: (1:1), ( 1 √ 2 : 2 √ 2 ), ( 2 √ 2 : 1 √ 2 ), (2:2) 32, 64, 128,

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]

Original: [Table] [Table]
Revised: [Table] [Table]