scholarly journals Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Fetulhak Abdurahman ◽  
Kinde Anlay Fante ◽  
Mohammed Aliy
Keyword(s):  
Object Detection ◽  
Malaria Parasite ◽  
Blood Smear ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Detection Accuracy ◽  
Small Object ◽  
Thick Blood Smear ◽  
Malaria Parasites ◽  
Microscopic Images

Abstract Background Manual microscopic examination of Leishman/Giemsa stained thin and thick blood smear is still the “gold standard” for malaria diagnosis. One of the drawbacks of this method is that its accuracy, consistency, and diagnosis speed depend on microscopists’ diagnostic and technical skills. It is difficult to get highly skilled microscopists in remote areas of developing countries. To alleviate this problem, in this paper, we propose to investigate state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from microscopic image of thick blood slides. Results YOLOV3 and YOLOV4 models, which are state-of-the-art object detectors in accuracy and speed, are not optimized for detecting small objects such as malaria parasites in microscopic images. We modify these models by increasing feature scale and adding more detection layers to enhance their capability of detecting small objects without notably decreasing detection speed. We propose one modified YOLOV4 model, called YOLOV4-MOD and two modified models of YOLOV3, which are called YOLOV3-MOD1 and YOLOV3-MOD2. Besides, new anchor box sizes are generated using K-means clustering algorithm to exploit the potential of these models in small object detection. The performance of the modified YOLOV3 and YOLOV4 models were evaluated on a publicly available malaria dataset. These models have achieved state-of-the-art accuracy by exceeding performance of their original versions, Faster R-CNN, and SSD in terms of mean average precision (mAP), recall, precision, F1 score, and average IOU. YOLOV4-MOD has achieved the best detection accuracy among all the other models with a mAP of 96.32%. YOLOV3-MOD2 and YOLOV3-MOD1 have achieved mAP of 96.14% and 95.46%, respectively. Conclusions The experimental results of this study demonstrate that performance of modified YOLOV3 and YOLOV4 models are highly promising for detecting malaria parasites from images captured by a smartphone camera over the microscope eyepiece. The proposed system is suitable for deployment in low-resource setting areas.

scholarly journals Malaria Parasite Detection in Thick Blood Smear Microscopic Images Using Modified YOLOV3 and YOLOV4 Models

2020 ◽  
Author(s):  
Fetulhak Abdurahman ◽  
Kinde Fante Anlay ◽  
Mohammed Aliy
Keyword(s):  
Object Detection ◽  
Malaria Parasite ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Malaria Diagnosis ◽  
Learning Ability ◽  
Background Information ◽  
Small Object ◽  
Microscopic Images ◽  
Resource Setting

Abstract Background Information: Manual microscopic examination is still the "golden standard" for malaria diagnosis. The challenge in the manual microscopy is the fact that its accuracy, consistency and speed of diagnosis depends on the skill of the laboratory technician. It is difficult to get highly skilled laboratory technicians in the remote areas of developing countries. In order to alleviate this problem, in this paper, we propose and investigate the state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from thick blood slides. Methods: YOLOV3 and YOLOV4 are state-of-the-art object detectors both in terms of accuracy and speed; however, they are not optimized for the detection of small objects such as malaria parasite in microscopic images. To deal with these challenges, we have modified YOLOV3 and YOLOV4 models by increasing the feature scale and by adding more detection layers, without notably decreasing their detection speed. We have proposed one modified YOLOV4 model, called YOLOV4-MOD and two modified models for YOLOV3, which are called YOLOV3-MOD1 and YOLOV3-MOD2. In addition, we have generated new anchor box scales and sizes by using the K-means clustering algorithm to exploit small object detection learning ability of the models.Results: The proposed modified YOLOV3 and YOLOV4 algorithms are evaluated on publicly available malaria dataset and achieve state-of-the-art accuracy by exceeding the performance of their original versions, Faster R-CNN and SSD in terms of mean average precision (mAP), recall, precision, F1 score, and average IOU. For 608 x 608 input resolution YOLOV4-MOD achieves the best detection performance among all the other models with mAP of 96.32%. For the same input resolution YOLOV3-MOD2 and YOLOV3-MOD1 achieved mAP of 96.14% and 95.46% respectively. Conclusions: Th experimental results demonstrate that the performance of the proposed modified YOLOV3 and YOLOV4 models are reliable to be applied for detection of malaria parasite from images that can be captured by smartphone camera over the microscope eyepiece. The proposed system can be easily deployed in low-resource setting and it can save lives.

scholarly journals Malaria Parasite Detection in Thick Blood Smear Microscopic Images Using Modified YOLOV3 and YOLOV4 Models

2020 ◽  
Author(s):  
Fetulhak Abdurahman ◽  
Kinde Fante Anlay ◽  
Mohammed Aliy
Keyword(s):  
Object Detection ◽  
Malaria Parasite ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Malaria Diagnosis ◽  
Learning Ability ◽  
Background Information ◽  
Small Object ◽  
Microscopic Images ◽  
Resource Setting

Abstract Background Information: Manual microscopic examination is still the ”golden standard” for malaria diagnosis. The challenge in the manual microscopy is the fact that its accuracy, consistency and speed of diagnosis depends on the skill of the laboratory technician. It is difficult to get highly skilled laboratory technicians in the remote areas of developing countries. In order to alleviate this problem, in this paper, we propose and investigate the state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from thick blood slides. Methods: YOLOV3 and YOLOV4 are state-of-the-art object detectors both in terms of accuracy and speed; however, they are not optimized for the detection of small objects such as malaria parasite in microscopic images. To deal with these challenges, we have modified YOLOV3 and YOLOV4 models by increasing the feature scale and by adding more detection layers, without notably decreasing their detection speed. We have proposed one modified YOLOV4 model, called YOLOV4-MOD and two modified models for YOLOV3, which are called YOLOV3-MOD1 and YOLOV3-MOD2. In addition, we have generated new anchor box scales and sizes by using the K-means clustering algorithm to exploit small object detection learning ability of the models. Results: The proposed modified YOLOV3 and YOLOV4 algorithms are evaluated on publicly available malaria dataset and achieve state-of-the-art accuracy by exceeding the performance of their original versions, Faster R-CNN and SSD in terms of mean average precision (mAP), recall, precision, F1 score, and average IOU. For 608 x 608 input resolution YOLOV4-MOD achieves the best detection performance among all the other models with mAP of 96.32%. For the same input resolution YOLOV3-MOD2 and YOLOV3-MOD1 achieved mAP of 96.14% and 95.46% respectively. Conclusions: Th experimental results demonstrate that the performance of the proposed modified YOLOV3 and YOLOV4 models are reliable to be applied for detection of malaria parasite from images that can be captured by smartphone camera over the microscope eyepiece. The proposed system can be easily deployed in low-resource setting and it can save lives.

scholarly journals Real-Time Small Drones Detection Based on Pruned YOLOv4

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3374
Author(s):  
Hansen Liu ◽  
Kuangang Fan ◽  
Qinghua Ouyang ◽  
Na Li
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Processing Speed ◽  
State Of The Art ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Small Object ◽  
Art Object ◽  
Real Time Detection ◽  
Small Object Detection

To address the threat of drones intruding into high-security areas, the real-time detection of drones is urgently required to protect these areas. There are two main difficulties in real-time detection of drones. One of them is that the drones move quickly, which leads to requiring faster detectors. Another problem is that small drones are difficult to detect. In this paper, firstly, we achieve high detection accuracy by evaluating three state-of-the-art object detection methods: RetinaNet, FCOS, YOLOv3 and YOLOv4. Then, to address the first problem, we prune the convolutional channel and shortcut layer of YOLOv4 to develop thinner and shallower models. Furthermore, to improve the accuracy of small drone detection, we implement a special augmentation for small object detection by copying and pasting small drones. Experimental results verify that compared to YOLOv4, our pruned-YOLOv4 model, with 0.8 channel prune rate and 24 layers prune, achieves 90.5% mAP and its processing speed is increased by 60.4%. Additionally, after small object augmentation, the precision and recall of the pruned-YOLOv4 almost increases by 22.8% and 12.7%, respectively. Experiment results verify that our pruned-YOLOv4 is an effective and accurate approach for drone detection.

scholarly journals A Robust Segmentation for Malaria Parasite Detection of Thick Blood Smear Microscopic Images

2019 ◽  
Vol 9 (4) ◽  
pp. 1450 ◽  
Author(s):  
Umi Salamah ◽  
Riyanarto Sarno ◽  
Agus Zainal Arifin ◽  
Anto Satriyo Nugroho ◽  
Ismail Ekoprayitno Rozi ◽  
...  
Keyword(s):  
Malaria Parasite ◽  
Blood Smear ◽  
Thick Blood Smear ◽  
Microscopic Images ◽  
Robust Segmentation ◽  
Parasite Detection

scholarly journals Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

2021 ◽  
Vol 11 (11) ◽  
pp. 4894
Author(s):  
Anna Scius-Bertrand ◽  
Michael Jungo ◽  
Beat Wolf ◽  
Andreas Fischer ◽  
Marc Bui
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Positive Impact ◽  
Detection System ◽  
Training Data ◽  
Detection Accuracy ◽  
Current State ◽  
Alignment Task ◽  
Scanned Image ◽  
Automatic Transcription

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

Small Targets Detection for Transmission Tower Based on SRGAN and Faster RCNN

2021 ◽  
Vol 14 ◽  
Author(s):  
Runze Liu ◽  
Guangwei Yan ◽  
Hui He ◽  
Yubin An ◽  
Ting Wang ◽  
...  
Keyword(s):  
Object Detection ◽  
Super Resolution ◽  
Generative Adversarial Networks ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Small Object ◽  
Detection Model ◽  
Tower Equipment ◽  
Small Object Detection ◽  
Small Targets

Background: Power line inspection is essential to ensure the safe and stable operation of the power system. Object detection for tower equipment can significantly improve inspection efficiency. However, due to the low resolution of small targets and limited features, the detection accuracy of small targets is not easy to improve. Objective: This study aimed to improve the tiny targets’ resolution while making the small target's texture and detailed features more prominent to be perceived by the detection model. Methods: In this paper, we propose an algorithm that employs generative adversarial networks to improve small objects' detection accuracy. First, the original image is converted into a super-resolution one by a super-resolution reconstruction network (SRGAN). Then the object detection framework Faster RCNN is utilized to detect objects on the super-resolution images. Result: The experimental results on two small object recognition datasets show that the model proposed in this paper has good robustness. It can especially detect the targets missed by Faster RCNN, which indicates that SRGAN can effectively enhance the detailed information of small targets by improving the resolution. Conclusion: We found that higher resolution data is conducive to obtaining more detailed information of small targets, which can help the detection algorithm achieve higher accuracy. The small object detection model based on the generative adversarial network proposed in this paper is feasible and more efficient. Compared with Faster RCNN, this model has better performance on small object detection.

scholarly journals Deep-Learning-Based Road Crack Detection Frameworks for Dashcam-captured Images under Different Illumination Conditions

2021 ◽  
Author(s):  
Da-Ren Chen ◽  
Wei-Min Chiu
Keyword(s):  
Object Detection ◽  
Large Scale ◽  
Crack Detection ◽  
State Of The Art ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
The Road ◽  
Art Object

Abstract Machine learning techniques have been used to increase detection accuracy of cracks in road surfaces. Most studies failed to consider variable illumination conditions on the target of interest (ToI), and only focus on detecting the presence or absence of road cracks. This paper proposes a new road crack detection method, IlumiCrack, which integrates Gaussian mixture models (GMM) and object detection CNN models. This work provides the following contributions: 1) For the first time, a large-scale road crack image dataset with a range of illumination conditions (e.g., day and night) is prepared using a dashcam. 2) Based on GMM, experimental evaluations on 2 to 4 levels of brightness are conducted for optimal classification. 3) the IlumiCrack framework is used to integrate state-of-the-art object detecting methods with CNN to classify the road crack images into eight types with high accuracy. Experimental results show that IlumiCrack outperforms the state-of-the-art R-CNN object detection frameworks.

scholarly journals Multiple-Oriented and Small Object Detection with Convolutional Neural Networks for Aerial Image

2019 ◽  
Vol 11 (18) ◽  
pp. 2176 ◽  
Author(s):  
Chen ◽  
Zhong ◽  
Tan
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Convolutional Neural Networks ◽  
Aerial Images ◽  
Superior Performance ◽  
Aerial Image ◽  
Detection Accuracy ◽  
Small Object ◽  
Data Set ◽  
Orientation Information

Detecting objects in aerial images is a challenging task due to multiple orientations and relatively small size of the objects. Although many traditional detection models have demonstrated an acceptable performance by using the imagery pyramid and multiple templates in a sliding-window manner, such techniques are inefficient and costly. Recently, convolutional neural networks (CNNs) have successfully been used for object detection, and they have demonstrated considerably superior performance than that of traditional detection methods; however, this success has not been expanded to aerial images. To overcome such problems, we propose a detection model based on two CNNs. One of the CNNs is designed to propose many object-like regions that are generated from the feature maps of multi scales and hierarchies with the orientation information. Based on such a design, the positioning of small size objects becomes more accurate, and the generated regions with orientation information are more suitable for the objects arranged with arbitrary orientations. Furthermore, another CNN is designed for object recognition; it first extracts the features of each generated region and subsequently makes the final decisions. The results of the extensive experiments performed on the vehicle detection in aerial imagery (VEDAI) and overhead imagery research data set (OIRDS) datasets indicate that the proposed model performs well in terms of not only the detection accuracy but also the detection speed.

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