Panoramik görüntüler üzerinden su altı hedef tespiti: DBSCAN ve derin öğrenme ağları ile bütünleşik bir yaklaşım
Underwater target detection via panoramic images: An integrated approach with DBSCAN and deep learning networks
Yazar: FATMA KÜBRA AKIN KÜÇÜK
Danışman: PROF. DR. İBRAHİM ÖZKOL
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Savunma Teknolojileri Ana Bilim Dalı / Savunma Teknolojileri Bilim Dalı
Derece: Yüksek Lisans, 2024
Konu: Savunma ve Savunma Teknolojileri = Defense and Defense Technologies

Özet:
Su altı akustik verileri, denizaltı savaşları, su altı hedef izleme, mayın tespiti, deniz
güvenliği, su altı haritalama ve çeşitli su altı araştırmalarında kritik rol oynayan önemli
bir çalışma konusudur. Bu alandaki sürekli gelişmeler, askeri amaçlı operasyonlardan
bilimsel araştırmalara kadar geniş bir yelpazede uygulamalara olanak tanımaktadır. Su
altı hedef tespiti, çeşitli sensörler, akustik sistemler ve görüntüleme teknikleri
kullanılarak gerçekleştirilen önemli bir akustik faaliyet alanıdır. Bu sensörler su
altındaki nesnelerin ses dalgalarını algılarlar. Böylece denizaltılar, torpidolar ve diğer
su altı araçları tarafından kullanılarak düşman veya potansiyel tehditleri belirleme
konusunda kritik bilgiler sağlarlar.
Son yıllarda su altı ekipman sistemlerinin gelişmesi ile birlikte bu alanda yapılan
çalışmalarda büyük bir artış meydana gelmiştir. Fakat, geniş su altı bölgelerindeki
karmaşık topografyalar ve değişen çevresel koşulları nedeniyle su altı hedef tespiti
kolay bir uygulama değildir ve çoğu zaman geleneksel tespit yöntemleri belirli bir
alanda çalışmakta ve değişen çevre koşullarına uyum sağlamakta yeterli
olmamaktadır. Bu nedenle bahsedilen zorluklarla baş edebilecek yeni yaklaşımların
geliştirilmesine yönelik çalışmalar artmıştır.
Bu tez çalışmasında, DBSCAN (Density-Based Spatial Clustering of Applications
with Noise) ve derin öğrenme ağlarının (yapay sinir ağlarının ve evrişimli sinir
ağlarının) birleştirildiği yenilikçi bir yaklaşım sunulmuştur.
Önerilen yaklaşımda simülasyon ortamında oluşturulmuş sonar verileri ve
beamforming algoritması kullanılarak açı, frekans ve gürültü seviyesinden oluşan
panoramik su altı resimleri oluşturulmuştur. Oluşturulan panorama resminde x
ekseninde derece cinsinde açı değerleri, y ekseninde Hz cinsinden frekans verileri ve
z ekseninde dB cinsinden gürültü seviyeleri bulunmaktadır. Oluşturulan veriler
işlenerek kullanıma hazır hale getirilmiştir. Bu kapsamda ilk olarak açı ve frekans
bandındaki ölçek farklılığını ortadan kaldırmak için frekans ve açı ekseni normalize
edilmiş ve iki eksende 0 ile 1 değerleri arasına getirilmiştir. Gürültü seviyeleri ise z
skor kullanılarak normalize edilmiştir. Veri işleme aşaması tamamlandıktan sonra
model kısmına geçilmiştir.
Sonar verilerinden oluşturulan panoramik deniz resimleri, ilk olarak DBSCAN
algoritmasından geçirilmiştir. DBSCAN algoritması, su altı panoramik görüntülerdeki
hedeflerin yoğunluk tabanlı bir şekilde kümelenmesini sağlar. Bu algoritma,
geleneksel tespit yöntemlerinin ötesine geçerek, hedeflerin doğal olarak oluşan
yoğunluk bölgelerinde daha etkili bir şekilde algılanmasını mümkün kılar.
DBSCAN'in sunduğu bu avantajı kullanmak ve sistem performansını optimize etmek
için, DBSCAN algoritması derin öğrenme ağlarıyla birleştirilmiştir. DBSCAN
tarafından kümelenen veriler yapay sinir ağlarına (ANN) ve evrişimli sinir ağlarına
(CNN) girdi olarak verilmiş ve modellerin hedef tespit performansları
değerlendirilmiştir.
Elde edilen sonuçlar sadece derin öğrenme ağları ile eğitilen modellerinin sonuçları ile
karşılaştırılmıştır. Önerilen yaklaşım , sadece panoromik görüntülerle eğitilen
modellerden, çok daha iyi performans elde etmiştir ve hedef tespiti konusunda daha
başarılı olmuştur. Bu sonuçlar, derin öğrenme ağlarının, DBSCAN tarafından
belirlenen yoğunluklu alanlarda daha spesifik ve hassas özellikler öğrenerek su altı
hedeflerini daha doğru bir şekilde tanıma yeteneğinin olduğunu göstermiştir.

Abstract:
Underwater acoustic data is an important subject of study that plays a critical role in
submarine warfare, underwater target tracking, mine detection, maritime security,
underwater mapping and various underwater research. The acoustic data collection
process is usually accomplished using specialized devices such as hydrophones or
sonar systems. The detected sound waves are recorded by the devices and stored in
digital data format. This data usually includes various characteristics such as sound
frequency, intensity, source and propagation path. The collected acoustic data is then
analyzed and interpreted. Continuous developments in this field enable a wide range
of applications, from military operations to scientific research.
Underwater target detection is an important field of activity using various sensors,
acoustic systems and imaging techniques. These sensors detect sound waves from
underwater objects. Therefore, they can be used by submarines, torpedoes and other
underwater vehicles, providing critical information to identify enemies or potential
threats. In recent years, with the development of underwater equipment systems, there
has been a great increase in studies in this field. One of the main challenges in
underwater target detection lies in the diverse and dynamic nature of the underwater
landscape. Unlike terrestrial environments, the underwater environment presents
unique obstacles such as acoustic reflection, signal attenuation, and unpredictable
hydrodynamic conditions. The complex topographies and changing environmental
conditions in large underwater regions, underwater target detection is not an easy
application, and often traditional detection methods are not sufficient to work in a
specific area and adapt to changing environmental conditions. For this reason, studies
on the development of new approaches that can cope with these challenges have
increased. Espacially, significant strides have been made in the field of underwater
target detection through the application of machine learning and artificial intelligence
(AI) techniques.
Deep learning algorithms, in particular, have demonstrated remarkable capabilities in
processing large volumes of acoustic data and extracting meaningful patterns for target
identification. By leveraging neural networks and deep architectures, researchers have
been able to achieve unprecedented levels of accuracy in underwater target detection
tasks.
In this thesis, an innovative approach that goes beyond deep learning methods, which
are new technologies, and combines DBSCAN (Density-Based Spatial Clustering of
Applications with Noise) and deep learning networks (artificial neural networks and
convolutional neural networks) is presented. First, the data produced in the simulation
environment was processed and made ready for training. Then, the training performance was tested and the results were compared with classical deep learning
methods.
In this approach, panoramic underwater images consisting of angle, frequency and
noise level were created using sonar data created in the simulation environment and
beamforming algorithm. Beamforming combines data from multiple sensors to create
a sonar beam focused in a specific direction. This process is used to determine the
positions of underwater objects and obtain high-resolution images. These images are
used to better understand underwater details and see objects more clearly. In the
panoromic images, the noise level in and around the angle axis where the target is
located is higher in all frequency axes than in other angles. This is important
information for target detection.
In the created panorama image, there are angle values in degrees on the x-axis,
frequency data in Hz on the y-axis, and noise levels in dB on the z-axis. In order to
eliminate the scale difference in the angle and frequency band, the frequency and angle
axis were normalized and brought to values between 0 and 1 on both axes. Noise levels
were normalized using z score.
Within the scope of this study, a total of 39 thousand panoramic data, including
untargeted, single-target and two-target, were created in the simulation environment,
and 33 thousand of these data were used as training data and 6 thousand as test
data.After the data processing stage was completed, the model part was started.
Panoramic sea images created from sonar data were first passed through the DBSCAN
algorithm. The DBSCAN algorithm provides density-based clustering of targets in
underwater panoramic images. This algorithm goes beyond traditional detection
methods, enabling more effective detection of targets in naturally occurring density
regions. DBSCAN separates data points into clusters, starting from seed points. It does
not require prior knowledge of the density and shape of data points. Therefore, it
provides an advantage in cases where the number and shape of clusters are unknown.
To use this advantage offered by DBSCAN and optimize system performance, the
DBSCAN algorithm is combined with deep learning networks.
Deep learning networks have been frequently used in the field of underwater target
detection in recent years due to their capacity to feed on large amounts of data. These
networks can learn complex patterns and detect targets based on data, thanks to their
multi-layered structure. In particular, deep learning models that perform target
detection by processing information from audio signals and panoramic sea images can
be very successful in detecting and classifying specific targets.
A key advantage of deep learning networks in the field of underwater target detection
is their data-driven feature extraction capabilities over traditional feature-based
approaches, which are often used to identify features that previously had to be
identified manually. In this way, the variability and complexities encountered in
underwater environments can be dealt with more effectively. ANN and CNN, which
are deep learning networks, are frequently used in target detection studies.
Convolutional Neural Networks (CNNs) are particularly effective in image processing
tasks. Such networks are specifically designed for identifying features and recognizing
objects in visual data. Artificial Neural Networks (ANNs) offer an ideal framework
for automatically extracting complex features and identifying more complex and
hierarchical patterns.Therefore, the data clustered by DBSCAN were given as input to artificial neural networks (ANN) and convolutional neural networks (CNN) and the
target detection performances of the models were evaluated.
In the testing phase, firstly, only the angle and frequency axes were normalized and
given as input to the ANN and CNN models, and then the training was repeated by
normalizing the noise levels. To test the proposed approach, z score calculations were
made on the data set and passed through the DBSCAN algorithm and given as input
to the ANN and CNN model. In addition, for comparison, z score calculations were
made and training was conducted with data that was not passed through DBSCAN.
The results obtained were compared with the results of models trained only with deep
learning networks, and the proposed approach achieved much better performance than
models trained only with panoramic images and was more successful in target
detection. Compared to the raw training data, an improvement of 76% for the ANN
model and 45% for the CNN model was achieved. It is expected that this amount of
improvement will increase by parameter optimization. These results also showed that
deep learning networks have better performance in underwater target detection by
learning specific and sensitive features with the DBSCAN algorithm.