Object Detection Using Clustering Algorithm Adaptive Searching Regions in Aerial Images

2020 ◽  
pp. 651-664
Author(s):  
Yi Wang ◽  
Youlong Yang ◽  
Xi Zhao
Keyword(s):  
Object Detection ◽  
Clustering Algorithm ◽  
Aerial Images

Train in Dense and Test in Sparse: A Method for Sparse Object Detection in Aerial Images

2020 ◽  
pp. 1-5
Author(s):  
Kun Ding ◽  
Guojin He ◽  
Huxiang Gu ◽  
Zisha Zhong ◽  
Shiming Xiang ◽  
...  
Keyword(s):  
Object Detection ◽  
Aerial Images

Tiny Object Detection in Aerial Images

2021 ◽  
Author(s):  
Jinwang Wang ◽  
Wen Yang ◽  
Haowen Guo ◽  
Ruixiang Zhang ◽  
Gui-Song Xia
Keyword(s):  
Object Detection ◽  
Aerial Images

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 Deep Learning-Based Object Detection for Unmanned Aerial Systems (UASs)-Based Inspections of Construction Stormwater Practices

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2834
Author(s):  
Billur Kazaz ◽  
Subhadipto Poddar ◽  
Saeed Arabi ◽  
Michael A. Perez ◽  
Anuj Sharma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Pollution Prevention ◽  
Aerial Images ◽  
Unmanned Aerial Systems ◽  
Construction Sites ◽  
State Regulations ◽  
Novel Approach ◽  
Model Training ◽  
Aerial Systems

Construction activities typically create large amounts of ground disturbance, which can lead to increased rates of soil erosion. Construction stormwater practices are used on active jobsites to protect downstream waterbodies from offsite sediment transport. Federal and state regulations require routine pollution prevention inspections to ensure that temporary stormwater practices are in place and performing as intended. This study addresses the existing challenges and limitations in the construction stormwater inspections and presents a unique approach for performing unmanned aerial system (UAS)-based inspections. Deep learning-based object detection principles were applied to identify and locate practices installed on active construction sites. The system integrates a post-processing stage by clustering results. The developed framework consists of data preparation with aerial inspections, model training, validation of the model, and testing for accuracy. The developed model was created from 800 aerial images and was used to detect four different types of construction stormwater practices at 100% accuracy on the Mean Average Precision (MAP) with minimal false positive detections. Results indicate that object detection could be implemented on UAS-acquired imagery as a novel approach to construction stormwater inspections and provide accurate results for site plan comparisons by rapidly detecting the quantity and location of field-installed stormwater practices.

Analysis Of High Resolution Aerial Images For Object Detection

1989 ◽  
Author(s):  
Mohan M. Trivedi ◽  
Amol G. Bokil ◽  
Mourad B. Takla ◽  
George B. Maksymonko ◽  
J. Thomas Broach
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Aerial Images

scholarly journals Extended Feature Pyramid Network with Adaptive Scale Training Strategy and Anchors for Object Detection in Aerial Images

2020 ◽  
Vol 12 (5) ◽  
pp. 784 ◽  
Author(s):  
Wei Guo ◽  
Weihong Li ◽  
Weiguo Gong ◽  
Jinkai Cui
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Semantic Information ◽  
Aerial Images ◽  
Training Strategy ◽  
The Public ◽  
Multi Scale ◽  
Shallow Layer ◽  
The Neural Network ◽  
Feature Pyramid

Multi-scale object detection is a basic challenge in computer vision. Although many advanced methods based on convolutional neural networks have succeeded in natural images, the progress in aerial images has been relatively slow mainly due to the considerably huge scale variations of objects and many densely distributed small objects. In this paper, considering that the semantic information of the small objects may be weakened or even disappear in the deeper layers of neural network, we propose a new detection framework called Extended Feature Pyramid Network (EFPN) for strengthening the information extraction ability of the neural network. In the EFPN, we first design the multi-branched dilated bottleneck (MBDB) module in the lateral connections to capture much more semantic information. Then, we further devise an attention pathway for better locating the objects. Finally, an augmented bottom-up pathway is conducted for making shallow layer information easier to spread and further improving performance. Moreover, we present an adaptive scale training strategy to enable the network to better recognize multi-scale objects. Meanwhile, we present a novel clustering method to achieve adaptive anchors and make the neural network better learn data features. Experiments on the public aerial datasets indicate that the presented method obtain state-of-the-art performance.

scholarly journals Computerized or Automated Object Recognition and Analysis of Pattern Matching in Runways Using Surface Track Data

2021 ◽  
Vol 23 (11) ◽  
pp. 159-165
Author(s):  
JAYANTH DWIJESH H P ◽  
◽  
SANDEEP S V ◽  
RASHMI S ◽  
◽  
...  
Keyword(s):  
Edge Detection ◽  
Object Detection ◽  
Pattern Matching ◽  
Aerial Images ◽  
3 Dimensional ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Detection Analysis ◽  
Efficient Detection ◽  
Minimal Damage

In today’s world, accurate and fast information is vital for safe aircraft landings. The purpose of an EMAS (Engineered Materials Arresting System) is to prevent an aeroplane from overrunning with no human injury and minimal damage to the aircraft. Although various algorithms for object detection analysis have been developed, only a few researchers have examined image analysis as a landing assist. Image intensity edges are employed in one system to detect the sides of a runway in an image sequence, allowing the runway’s 3-dimensional position and orientation to be approximated. A fuzzy network system is used to improve object detection and extraction from aerial images. In another system, multi-scale, multiplatform imagery is used to combine physiologically and geometrically inspired algorithms for recognizing objects from hyper spectral and/or multispectral (HS/MS) imagery. However, the similarity in the top view of runways, buildings, highways, and other objects is a disadvantage of these methods. We propose a new method for detecting and tracking the runway based on pattern matching and texture analysis of digital images captured by aircraft cameras. Edge detection techniques are used to recognize runways from aerial images. The edge detection algorithms employed in this paper are the Hough Transform, Canny Filter, and Sobel Filter algorithms, which result in efficient detection.

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