scholarly journals Image Segmentation Method Selection for Vehicle Detection Using Unmanned Aerial Vehicle

2015 ◽  
Vol 9 (5) ◽  
Author(s):  
Kirill Viktorovich Abramov ◽  
Pavel Vyacheclavovich Skribtsov ◽  
Pavel Alexandrovich Kazantsev
Keyword(s):  
Image Segmentation ◽  
Unmanned Aerial Vehicle ◽  
Vehicle Detection ◽  
Segmentation Method ◽  
Method Selection ◽  
Aerial Vehicle ◽  
Selection For

Road Image Segmentation using Unmanned Aerial Vehicle Images and DeepLab V3+ Semantic Segmentation Model

2021 ◽  
Author(s):  
Mat Nizam Mahmud ◽  
Muhammad Khusairi Osman ◽  
Ahmad Puad Ismail ◽  
Fadzil Ahmad ◽  
Khairul Azman Ahmad ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Unmanned Aerial Vehicle ◽  
Semantic Segmentation ◽  
Aerial Vehicle

scholarly journals Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation

2019 ◽  
Vol 11 (9) ◽  
pp. 1046 ◽  
Author(s):  
Heming Jia ◽  
Zhikai Xing ◽  
Wenlong Song
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Optimization Algorithm ◽  
Oil Pollution ◽  
Three Dimensional ◽  
Oil Field ◽  
Segmentation Method ◽  
Pulse Coupled Neural Network ◽  
Aerial Vehicle ◽  
Pollution Area

This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the oil field area. The UAV is good at shooting the ground area, but its ability to identify the oil pollution area is poor. In order to solve this problem, a 3DPCNN-HSOA algorithm is proposed to segment the oil pollution image, and the oil pollution area is segmented to identify the dirty oil area and improve the inspection of environmental pollution. The 3DPCNN image segmentation method has simple structure and good segmentation effect, but it has many parameters and poor segmentation effect for complex oil images. Therefore, we apply HSOA algorithm to optimize the parameters of 3DPCNN algorithm, so as to improve the segmentation accuracy and solve the segmentation of oil pollution images. The experimental results show that the 3DPCNN-HSOA model can separate the oil pollution area from the complex background.

Performance Assessment of Sky Segmentation Approaches for UAVs

2019 ◽  
Vol 19 (04) ◽  
pp. 1950023
Author(s):  
Ahmed S. Mashaly
Keyword(s):  
Image Segmentation ◽  
Performance Assessment ◽  
General Purpose ◽  
Segmentation Method ◽  
Research Fields ◽  
Different Types ◽  
Segmentation Approach ◽  
Aerial Vehicle ◽  
Computer Vision Systems ◽  
Overall Performance

Image segmentation is one of the most challenging research fields for both image analysis and interpretation. The applications of image segmentation could be found as the primary step in various computer vision systems. Therefore, the choice of a reliable and accurate segmentation method represents a non-trivial task. Since the selected image segmentation method influences the overall performance of the remaining system steps, sky segmentation appears as a vital step for Unmanned Aerial Vehicle (UAV) autonomous obstacle avoidance missions. In this paper, we are going to introduce a comprehensive literature survey of the different types of image segmentation methodology followed by a detailed illustration of the general-purpose methods and the state-of-art sky segmentation approaches. In addition, we introduce an improved version of our previously published work for sky segmentation purpose. The performance of the proposed sky segmentation approach is compared with various image segmentation approaches using different parameters and datasets. For performance assessment, we test our approach under different situations and compare its performance with commonly used approaches in terms of several assessment indexes. From the experimental results, the proposed method gives promising results compared with the other image segmentation approaches.

Image Segmentation-Based Unmanned Aerial Vehicle Safe Navigation

2017 ◽  
Vol 14 (7) ◽  
pp. 391-410
Author(s):  
Pooja Agrawal ◽  
Ashwini Ratnoo ◽  
Debasish Ghose
Keyword(s):  
Image Segmentation ◽  
Unmanned Aerial Vehicle ◽  
Safe Navigation ◽  
Aerial Vehicle

Acoustical unmanned aerial vehicle detection in indoor scenarios using logistic regression model

2020 ◽  
pp. 1351010X2091785
Author(s):  
Gino Iannace ◽  
Giuseppe Ciaburro ◽  
Amelia Trematerra
Keyword(s):  
Logistic Regression ◽  
Unmanned Aerial Vehicle ◽  
Indoor Environment ◽  
Vehicle Detection ◽  
Sound Level ◽  
Shopping Center ◽  
Model Based ◽  
Aerial Vehicle ◽  
Level Meter ◽  
Indoor Scenarios

In this study, the data obtained from the acoustic measurements were used to train a model based on logistic regression in order to detect a quadrotor’s vehicle in indoor environment. To simulate a real environment, we made sound recordings in a shopping center. The sounds related to two scenarios were recorded: only anthropic noise and anthropic noise with background music. Later, we reproduced these sounds in an indoor environment of the same size and characteristics as the shopping center. During the simulation test, a drone placed at different distances from the sound level meter was turned on at different speeds to identify their presence in complex acoustic scenarios. Subsequently, these measurements were used to implement a model based on logistic regression for the automatic detection of the unmanned aerial vehicle. Logistic regression is widely used in pattern recognition of the binary dependent variable. This model returns high value of accuracy (0.994), indicating a high number of correct detections. The results obtained in this study suggest the use of this tool for unmanned aerial vehicle detection applications.

scholarly journals Affine-Function Transformation-Based Object Matching for Vehicle Detection from Unmanned Aerial Vehicle Imagery

2019 ◽  
Vol 11 (14) ◽  
pp. 1708 ◽  
Author(s):  
Shuang Cao ◽  
Yongtao Yu ◽  
Haiyan Guan ◽  
Daifeng Peng ◽  
Wanqian Yan
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vehicle Detection ◽  
Location Estimation ◽  
Affine Function ◽  
Response Elimination ◽  
Object Matching ◽  
Function Transformation ◽  
Segmentation Strategy ◽  
Aerial Vehicle ◽  
Uav Images

Vehicle detection from remote sensing images plays a significant role in transportation related applications. However, the scale variations, orientation variations, illumination variations, and partial occlusions of vehicles, as well as the image qualities, bring great challenges for accurate vehicle detection. In this paper, we present an affine-function transformation-based object matching framework for vehicle detection from unmanned aerial vehicle (UAV) images. First, meaningful and non-redundant patches are generated through a superpixel segmentation strategy. Then, the affine-function transformation-based object matching framework is applied to a vehicle template and each of the patches for vehicle existence estimation. Finally, vehicles are detected and located after matching cost thresholding, vehicle location estimation, and multiple response elimination. Quantitative evaluations on two UAV image datasets show that the proposed method achieves an average completeness, correctness, quality, and F1-measure of 0.909, 0.969, 0.883, and 0.938, respectively. Comparative studies also demonstrate that the proposed method achieves compatible performance with the Faster R-CNN and outperforms the other eight existing methods in accurately detecting vehicles of various conditions.

Sign in / Sign up

Export Citation Format

Share Document