382 research outputs found
Thermal-Infrared Remote Target Detection System for Maritime Rescue based on Data Augmentation with 3D Synthetic Data
This paper proposes a thermal-infrared (TIR) remote target detection system
for maritime rescue using deep learning and data augmentation. We established a
self-collected TIR dataset consisting of multiple scenes imitating human rescue
situations using a TIR camera (FLIR). Additionally, to address dataset scarcity
and improve model robustness, a synthetic dataset from a 3D game (ARMA3) to
augment the data is further collected. However, a significant domain gap exists
between synthetic TIR and real TIR images. Hence, a proper domain adaptation
algorithm is essential to overcome the gap. Therefore, we suggest a domain
adaptation algorithm in a target-background separated manner from 3D
game-to-real, based on a generative model, to address this issue. Furthermore,
a segmentation network with fixed-weight kernels at the head is proposed to
improve the signal-to-noise ratio (SNR) and provide weak attention, as remote
TIR targets inherently suffer from unclear boundaries. Experiment results
reveal that the network trained on augmented data consisting of translated
synthetic and real TIR data outperforms that trained on only real TIR data by a
large margin. Furthermore, the proposed segmentation model surpasses the
performance of state-of-the-art segmentation methods.Comment: 12 page
Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review
Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle
sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and
foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object
detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages
and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image
types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In
particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and
compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of
these approaches. The arti
Feature-based object tracking in maritime scenes.
A monitoring of presence, location and activity of various objects on the sea is essential for maritime navigation and collision avoidance. Mariners normally rely on two complementary methods of the monitoring: radar and satellite-based aids and human observation. Though radar aids are relatively accurate at long distances, their capability of detecting small, unmanned or non-metallic craft that generally do not reflect radar waves sufficiently enough, is limited. The mariners, therefore, rely in such cases on visual observations. The visual observation is often facilitated by using cameras overlooking the sea that can also provide intensified infra-red images. These systems or nevertheless merely enhance the image and the burden of the tedious and error-prone monitoring task still rests with the operator. This thesis addresses the drawbacks of both methods by presenting a
framework consisting of a set of machine vision algorithms that facilitate the monitoring tasks in maritime environment. The framework detects and tracks objects in a sequence of images captured by a camera mounted either on a board of a vessel or on a static platform over-looking the sea. The detection of objects is independent of their appearance and conditions such as weather and time of the day. The output of the framework consists of locations and motions of all detected objects with respect to a fixed
point in the scene. All values are estimated in real-world units, i. e. location is expressed in metres and velocity in knots. The consistency of the estimates is maintained by compensating for spurious effects such as vibration of the
camera. In addition, the framework continuously checks for predefined events such as collision threats or area intrusions, raising an alarm when any such event
occurs. The development and evaluation of the framework is based on sequences captured under conditions corresponding to a designated application. The independence of the detection and tracking on the appearance of the sceneand
objects is confirmed by a final cross-validation of the framework on previously unused sequences. Potential applications of the framework in various areas of maritime environment including navigation, security, surveillance and others are outlined. Limitations to the presented framework are identified and possible solutions suggested. The thesis concludes with suggestions to further directions of the research presented
Ship recognition on the sea surface using aerial images taken by Uav : a deep learning approach
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesOceans are very important for mankind, because they are a very important source of
food, they have a very large impact on the global environmental equilibrium, and it is
over the oceans that most of the world commerce is done. Thus, maritime surveillance
and monitoring, in particular identifying the ships used, is of great importance to
oversee activities like fishing, marine transportation, navigation in general, illegal
border encroachment, and search and rescue operations. In this thesis, we used images
obtained with Unmanned Aerial Vehicles (UAVs) over the Atlantic Ocean to identify
what type of ship (if any) is present in a given location. Images generated from UAV
cameras suffer from camera motion, scale variability, variability in the sea surface and
sun glares. Extracting information from these images is challenging and is mostly done
by human operators, but advances in computer vision technology and development of
deep learning techniques in recent years have made it possible to do so automatically.
We used four of the state-of-art pretrained deep learning network models, namely
VGG16, Xception, ResNet and InceptionResNet trained on ImageNet dataset, modified
their original structure using transfer learning based fine tuning techniques and then
trained them on our dataset to create new models. We managed to achieve very high
accuracy (99.6 to 99.9% correct classifications) when classifying the ships that appear
on the images of our dataset. With such a high success rate (albeit at the cost of high
computing power), we can proceed to implement these algorithms on maritime patrol
UAVs, and thus improve Maritime Situational Awareness
Modelling false positive reduction in maritime object detection
Target detection has become a very significant research area in computer vision with its applications in military, maritime surveillance, and defense and security. Maritime target detection during critical sea conditions produces a number of false positives when using the existing algorithms due to sea waves, dynamic nature of the ocean, camera motion, sea glint, sensor noise, sea spray, swell and the presence of birds. The main question that has been addressed in this research is how can object detection be improved in maritime environment by reducing false positives and promoting detection rate. Most of Previous work on object detection still fails to address the problem of false positives and false negatives due to background clutter. Most of the researchers tried to reduce false positives by applying filters but filtering degrades the quality of an image leading to more false alarms during detection. As much as radar technology has previously been the most utilized method, it still fails to detect very small objects and it may be applied in special circumstances. In trying to improve the implementation of target detection in maritime, empirical research method was proposed to answer questions about existing target detection algorithms and techniques used to reduce false positives in object detection. Visible images were retrained on a pre-trained Faster R-CNN with inception v2. The pre-trained model was retrained on five different sample data with increasing size, however for the last two samples the data was duplicated to increase size. For testing purposes 20 test images were utilized to evaluate all the models. The results of this study showed that the deep learning method used performed best in detecting maritime vessels and the increase of dataset improved detection performance and false positives were reduced. The duplication of images did not yield the best results; however, the results were promising for the first three models with increasing data
Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons
With the advancement of maritime unmanned aerial vehicles (UAVs) and deep
learning technologies, the application of UAV-based object detection has become
increasingly significant in the fields of maritime industry and ocean
engineering. Endowed with intelligent sensing capabilities, the maritime UAVs
enable effective and efficient maritime surveillance. To further promote the
development of maritime UAV-based object detection, this paper provides a
comprehensive review of challenges, relative methods, and UAV aerial datasets.
Specifically, in this work, we first briefly summarize four challenges for
object detection on maritime UAVs, i.e., object feature diversity, device
limitation, maritime environment variability, and dataset scarcity. We then
focus on computational methods to improve maritime UAV-based object detection
performance in terms of scale-aware, small object detection, view-aware,
rotated object detection, lightweight methods, and others. Next, we review the
UAV aerial image/video datasets and propose a maritime UAV aerial dataset named
MS2ship for ship detection. Furthermore, we conduct a series of experiments to
present the performance evaluation and robustness analysis of object detection
methods on maritime datasets. Eventually, we give the discussion and outlook on
future works for maritime UAV-based object detection. The MS2ship dataset is
available at
\href{https://github.com/zcj234/MS2ship}{https://github.com/zcj234/MS2ship}.Comment: 32 pages, 18 figure
Modelling false positive reduction in maritime object detection
Target detection has become a very significant research area in computer vision with its applications in military, maritime surveillance, and defense and security. Maritime target detection during critical sea conditions produces a number of false positives when using the existing algorithms due to sea waves, dynamic nature of the ocean, camera motion, sea glint, sensor noise, sea spray, swell and the presence of birds. The main question that has been addressed in this research is how can object detection be improved in maritime environment by reducing false positives and promoting detection rate. Most of Previous work on object detection still fails to address the problem of false positives and false negatives due to background clutter. Most of the researchers tried to reduce false positives by applying filters but filtering degrades the quality of an image leading to more false alarms during detection. As much as radar technology has previously been the most utilized method, it still fails to detect very small objects and it may be applied in special circumstances. In trying to improve the implementation of target detection in maritime, empirical research method was proposed to answer questions about existing target detection algorithms and techniques used to reduce false positives in object detection. Visible images were retrained on a pre-trained Faster R-CNN with inception v2. The pre-trained model was retrained on five different sample data with increasing size, however for the last two samples the data was duplicated to increase size. For testing purposes 20 test images were utilized to evaluate all the models. The results of this study showed that the deep learning method used performed best in detecting maritime vessels and the increase of dataset improved detection performance and false positives were reduced. The duplication of images did not yield the best results; however, the results were promising for the first three models with increasing data
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
In this paper, we present a Large-Scale and high-diversity general Thermal
InfraRed (TIR) Object Tracking Benchmark, called LSOTBTIR, which consists of an
evaluation dataset and a training dataset with a total of 1,400 TIR sequences
and more than 600K frames. We annotate the bounding box of objects in every
frame of all sequences and generate over 730K bounding boxes in total. To the
best of our knowledge, LSOTB-TIR is the largest and most diverse TIR object
tracking benchmark to date. To evaluate a tracker on different attributes, we
define 4 scenario attributes and 12 challenge attributes in the evaluation
dataset. By releasing LSOTB-TIR, we encourage the community to develop deep
learning based TIR trackers and evaluate them fairly and comprehensively. We
evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of
baselines, and the results show that deep trackers achieve promising
performance. Furthermore, we re-train several representative deep trackers on
LSOTB-TIR, and their results demonstrate that the proposed training dataset
significantly improves the performance of deep TIR trackers. Codes and dataset
are available at https://github.com/QiaoLiuHit/LSOTB-TIR.Comment: accepted by ACM Mutlimedia Conference, 202
- …