An Improved Faster R-CNN for Small Object Detection

Cao, Chuanbao; Wang, Bo; Zhang, Wenrui; Zeng, Xiaodong; Xu, Yan; Feng, Zhouming; Liu, Yutao; Wu, Zengyan

doi:10.1109/access.2019.2932731

Cited by 169 publications

(101 citation statements)

References 12 publications

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“…Lastly, although we used the FRCNN architecture in the present study, we need to carefully choose the best method for achieving our goal, because deep learning technologies have recently been progressing massively [ 51 ]. In particular, FRCNN has been reported to have difficulty identifying objects from low-resolution images, due to its weak capacity to identify local texture [ 52 ]. We plan to improve the algorithm appropriately, according to the direction of our social implementation.…”

Section: Discussionmentioning

confidence: 99%

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

Jinnai¹,

Yamazaki²,

Hirano

et al. 2020

Biomolecules

176

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Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (p = 0.0081) and 75.1% (p < 0.00001), respectively. For two-class classification (benign or malignant), the accuracy, sensitivity, and specificity were 91.5%, 83.3%, and 94.5% by FRCNN; 86.6%, 86.3%, and 86.6% by BCD; and 85.3%, 83.5%, and 85.9% by TRN, respectively. False positive rates and positive predictive values were 5.5% and 84.7% by FRCNN, 13.4% and 70.5% by BCD, and 14.1% and 68.5% by TRN, respectively. We compared the classification performance of FRCNN with 20 dermatologists. As a result, the classification accuracy of FRCNN was better than that of the dermatologists. In the future, we plan to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer.

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Section: Discussionmentioning

confidence: 99%

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

Jinnai¹,

Yamazaki²,

Hirano

et al. 2020

Biomolecules

176

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“…Areas of future research to expand this technique include testing whether there are performance improvements for detecting elephants by including the near infrared band and testing for which other species this is already a viable monitoring technique. More broadly, deep learning methods for detecting small objects can be further improved [83,84] and large training datasets containing images of wildlife from an aerial perspective should be developed. If satellite monitoring is applied at scale then developing methods to ensure standardised and occasional ground-truthing will be required to ensure image interpretation is accurate [25].…”

Section: Discussionmentioning

confidence: 99%

Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

Duporge

Isupova

Reece

et al. 2020

Preprint

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Satellites allow large-scale surveys to be conducted in short time periods with repeat surveys possible <24hrs. Very high-resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, homogeneous landscapes and seascapes where target animals have a strong contrast with their environment. However, no research to date has detected animals in complex heterogeneous environments or detected elephants from space using very high-resolution satellite imagery and deep learning.In this study we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView-3 and 4 satellite data – the highest resolution satellite imagery commercially available. We train and test the model on eleven images from 2014-2019. We compare the performance accuracy of the CNN against human accuracy. Additionally, we apply the model on a coarser resolution satellite image (GeoEye-1) captured in Kenya to test if the algorithm can generalise to an elephant population outside of the training area.Our results show the CNN performs with high accuracy, comparable to human detection capabilities. The detection accuracy (i.e., F2 score) of the CNN models was 0.78 in heterogeneous areas and 0.73 in homogenous areas. This compares with the detection accuracy of the human labels with an averaged F2 score 0.77 in heterogeneous areas and 0.80 in homogenous areas. The CNN model can generalise to detect elephants in a different geographical location and from a lower resolution satellite.Our study demonstrates the feasibility of applying state-of-the-art satellite remote sensing and deep learning technologies for detecting and counting African elephants in heterogeneous landscapes. The study showcases the feasibility of using high resolution satellite imagery as a promising new wildlife surveying technique. Through creation of a customised training dataset and application of a Convolutional Neural Network, we have automated the detection of elephants in satellite imagery with as high accuracy as human detection capabilities. The success of the model to detect elephants outside of the training data site demonstrates the generalisability of the technique.

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“…Deep learning methods are categorized into two groups: CNNs and RNNs [ 4 , 5 , 11 , 13 , 16 , 20 , 42 ]. In our surveys, R-CNN and Fast R-CNN are slower than YOLO and SSD in training, while the latter is proved to be with lower accuracy [ 6 – 8 , 21 , 22 , 32 , 33 , 39 , 40 , 56 ]. Hence, training a classifier based on Faster R-CNN to conduct apple ripeness classification is regarded as an optimal method.…”

Section: Related Workmentioning

confidence: 99%

Apple Ripeness Identification Using Deep Learning

Xiao

Nguyen

Yan

2021

Communications in Computer and Information Science

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Deep learning models assist us in fruit classification, which allow us to use digital images from cameras to classify a fruit and find its class of ripeness automatically. Apple ripeness classification is a problem in computer vision and deep learning for pattern classification. In this paper, the ripeness of apples in digital images will be classified by using convolutional neural networks (CNN or ConvNets) in deep learning. The goal of this project is to verify the capability of deep learning models for fruit classification so as to lessen our human labor. Our experiments consist of four parts, namely, image preprocessing, apple detection, ripeness classification, and resultant evaluations. The contribution of this paper is that the classifiers are able to achieve the best result, i.e., the ripeness class of an apple from a given digital image is able to be precisely predicted. We have optimized the deep learning models and trained the classifiers so as to achieve the best outcome.

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An Improved Faster R-CNN for Small Object Detection

Cited by 169 publications

References 12 publications

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

The Development of a Skin Cancer Classification System for Pigmented Skin Lesions Using Deep Learning

Using very high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

Apple Ripeness Identification Using Deep Learning

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