AUTOMATIC SEA BIRD DETECTION FROM HIGH RESOLUTION AERIAL IMAGERY

2016 ◽

Vol XLI-B7 ◽

pp. 299-303

Author(s):

S. Mader ◽

G. J. Grenzdörffer

Keyword(s):

Image Processing ◽

Marine Mammals ◽

Feature Detection ◽

Bird Species ◽

Automatic Monitoring ◽

Aerial Imagery ◽

Aerial Images ◽

Processing Methods ◽

Bird Detection ◽

Global Operations

Great efforts are presently taken in the scientific community to develop computerized and (fully) automated image processing methods allowing for an efficient and automatic monitoring of sea birds and marine mammals in ever-growing amounts of aerial imagery. Currently the major part of the processing, however, is still conducted by especially trained professionals, visually examining the images and detecting and classifying the requested subjects. This is a very tedious task, particularly when the rate of void images regularly exceeds the mark of 90%. In the content of this contribution we will present our work aiming to support the processing of aerial images by modern methods from the field of image processing. We will especially focus on the combination of local, region-based feature detection and piecewise global image segmentation for automatic detection of different sea bird species. Large image dimensions resulting from the use of medium and large-format digital cameras in aerial surveys inhibit the applicability of image processing methods based on global operations. In order to efficiently handle those image sizes and to nevertheless take advantage of globally operating segmentation algorithms, we will describe the combined usage of a simple performant feature detector based on local operations on the original image with a complex global segmentation algorithm operating on extracted sub-images. The resulting exact segmentation of possible candidates then serves as a basis for the determination of feature vectors for subsequent elimination of false candidates and for classification tasks.

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Domain randomization-enhanced deep learning models for bird detection

Scientific Reports ◽

10.1038/s41598-020-80101-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Xin Mao ◽

Jun Kang Chow ◽

Pin Siang Tan ◽

Kuan-fu Liu ◽

Jimmy Wu ◽

...

Keyword(s):

Deep Learning ◽

Continuous Monitoring ◽

Bird Species ◽

Training Data ◽

Learning Models ◽

Fine Grained ◽

Bird Detection ◽

Relationship Of ◽

The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

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IMG2nDSM: Height Estimation from Single Airborne RGB Images with Deep Learning

Remote Sensing ◽

10.3390/rs13122417 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2417

Author(s):

Savvas Karatsiolis ◽

Andreas Kamilaris ◽

Ian Cole

Keyword(s):

Deep Learning ◽

State Of The Art ◽

Aerial Imagery ◽

Aerial Images ◽

Surface Model ◽

Large Area ◽

Digital Terrain ◽

Terrain Models ◽

Architectural Features ◽

Rgb Images

Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.

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MTI-YOLO: A Light-Weight and Real-Time Deep Neural Network for Insulator Detection in Complex Aerial Images

Energies ◽

10.3390/en14051426 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1426

Author(s):

Chuanyang Liu ◽

Yiquan Wu ◽

Jingjing Liu ◽

Jiaming Han

Keyword(s):

Feature Detection ◽

Feature Fusion ◽

Memory Storage ◽

Aerial Images ◽

Detection Accuracy ◽

Composite Insulator ◽

Running Time ◽

Scale Feature ◽

Multi Scale ◽

Good Trade

Insulator detection is an essential task for the safety and reliable operation of intelligent grids. Owing to insulator images including various background interferences, most traditional image-processing methods cannot achieve good performance. Some You Only Look Once (YOLO) networks are employed to meet the requirements of actual applications for insulator detection. To achieve a good trade-off among accuracy, running time, and memory storage, this work proposes the modified YOLO-tiny for insulator (MTI-YOLO) network for insulator detection in complex aerial images. First of all, composite insulator images are collected in common scenes and the “CCIN_detection” (Chinese Composite INsulator) dataset is constructed. Secondly, to improve the detection accuracy of different sizes of insulator, multi-scale feature detection headers, a structure of multi-scale feature fusion, and the spatial pyramid pooling (SPP) model are adopted to the MTI-YOLO network. Finally, the proposed MTI-YOLO network and the compared networks are trained and tested on the “CCIN_detection” dataset. The average precision (AP) of our proposed network is 17% and 9% higher than YOLO-tiny and YOLO-v2. Compared with YOLO-tiny and YOLO-v2, the running time of the proposed network is slightly higher. Furthermore, the memory usage of the proposed network is 25.6% and 38.9% lower than YOLO-v2 and YOLO-v3, respectively. Experimental results and analysis validate that the proposed network achieves good performance in both complex backgrounds and bright illumination conditions.

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Comparison of Different Image Processing Methods with Spatial Information in Clinical Brain MRI

Proceedings of the 2020 12th International Conference on Bioinformatics and Biomedical Technology ◽

10.1145/3405758.3405786 ◽

2020 ◽

Author(s):

Iza Sazanita Isa ◽

Mohamad Khairul Faizi Mat Saad ◽

Muhammad Haris Khusairi Mohmad Kadir ◽

Ahmad Afifi Ahmad Afandi ◽

Noor Khairiah A. Karim ◽

...

Keyword(s):

Image Processing ◽

Spatial Information ◽

Brain Mri ◽

Processing Methods

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Image Processing Methods of LANDSAT TM Data for Lineament Analysis

GEOINFORMATICS ◽

10.6010/geoinformatics1975.1989.14b_25 ◽

1989 ◽

Vol 1989 (14B) ◽

pp. 25-39

Author(s):

Katsuaki KOIKE ◽

Hiroyuki ITOH ◽

Michito OHMI

Keyword(s):

Image Processing ◽

Landsat Tm ◽

Processing Methods ◽

Lineament Analysis

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Fast Transforms in Image Processing: Compression, Restoration, and Resampling

Advances in Electrical Engineering ◽

10.1155/2014/276241 ◽

2014 ◽

Vol 2014 ◽

pp. 1-23 ◽

Cited By ~ 6

Author(s):

Leonid P. Yaroslavsky

Keyword(s):

Image Processing ◽

Image Compression ◽

Image Restoration ◽

Compressive Sensing ◽

Adaptive Filters ◽

Processing Methods ◽

Fast Transforms ◽

Image Transforms

Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet, and alike. They proved to be very efficient in image compression, in image restoration, in image resampling, and in geometrical transformations and can be traced back to early 1970s. The paper reviews these methods, with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive filters for image restoration and up to “compressive sensing” methods that gained popularity in last few years. References are made to both first publications of the corresponding results and more recent and more easily available ones. The review has a tutorial character and purpose.

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