A Sensor System for Detection of Hull Surface Defects

This paper presents a sensor system for detecting defects in ship hull surfaces. The sensor was developed to enable a robotic system to perform grit blasting operations on ship hulls. To achieve this, the proposed sensor system captures images with the help of a camera and processes them in real time using a new defect detection method based on thresholding techniques. What makes this method different is its efficiency in the automatic detection of defects from images recorded in variable lighting conditions. The sensor system was tested under real conditions at a Spanish shipyard, with excellent results

A Comparison of Fixed Threshold CFAR and CNN Ship Detection Methods for S-band NovaSAR Images

NovaSAR is a commercial S-band Synthetic Aperture Radar (SAR) small satellite, built and operated by SSTL in the UK. One of its primary mission objectives is to carry out maritime surveillance and monitoring for security and defence applications. An investigation was carried out into comparing and contrasting conventional and new methods to perform automated ship detection in NovaSAR images. The outcome of this investigation could show the potential effectiveness of ship detection using spaceborne S-band SAR for Maritime Domain Awareness (MDA). The conventional approach is to apply a suitable distribution model to characterise sea surface clutter, followed by the implementation of a fixed threshold, Constant False Alarm Rate (CFAR) detection algorithm. In comparison, a RetinaNet-based convolutional neural network (CNN)solution was developed and trained on an open-source C-band dataset in order to determine the validity of applying non-native training data to S-band imagery. The detection performance was then compared with the CFAR technique, finding that for two selected test acquisitions a CNN-based ship detection algorithm was able to outperform a fixed threshold, CFAR-based method in the absence of native training data. CNN ship detection performance was further improved by applying transfer learning to a native S-band NovaSAR image dataset

Wavelet based Fault Detection Method for Ungrounded Power System with Balanced and Unbalanced load

Modern spectral and harmonic analysis is based on Fourier transforms. However, these techniques are less efficient in tracking the signal dynamics for transient disturbances. Consequently, the wavelet transform has been introduced as an adaptable technique for non-stationary signal analysis. Although the application of wavelets in the area of power system engineering is still relatively new, it is evolving very rapidly. In this paper wavelet based method for detection of faults in an ungrounded integrated power system (IPS) of Navy ships is proposed. However the “Virtual ground” exists between the modules of IPS and ship hull, because of insulation capacitance of the cable and the EMI filters between the modules of the IPS. The fault current is very low for a single line to ground fault in this ungrounded system allowing continuous operation but also making fault detection difficult. The proposed method uses wavelets for detection of ground fault in ungrounded power system. The ground fault conditions are simulated using MATLAB-SIMULINK and fault detection implemented with Daubechies wavelets. It is shown that transient ground faults can be detected by wavelet analysis of the line to line voltages when ship load is balanced and unbalanced. Verification of the proposed method has been done by simulating fault between a line and ship hull and analyzing the results.DOI:http://dx.doi.org/10.11591/ijece.v1i1.5

A New Calculation Model of Detection Time for Heat Detector in Long and Narrow Space

AbstractFire detector plays an important role in ship fire safety system. Usually, there are two types of fire detectors including fire smoke detector and heat detector, which are widely used in exit passageway, corridor, ladderway, and other long and narrow spaces in ship. Due to the smoke plume characteristics in these limited spaces are different to that of free plume in open place, the detection time calculating model of heat detector for these two different conditions are consequently different. This work is to develop a new detection time calculating model based on the fire plume rules including characteristics of temperature and velocity of fire smoke distributing in long and narrow spaces. Numeric method for calculating detection time is also presented. Finally, some calculations and analysis for a given fire scenario are performed. The numeric results are compared with that of the existing detection time calculating model based on free plume theory, which demonstrate the applicability of the model proposed in this article

An energy consumption approach to estimate air emission reductions in container shipping

Container shipping is the largest producer of emissions within the maritime shipping industry. Hence, measures have been designed and implemented to reduce ship emission levels. IMO's MARPOL Annex VI, with its future plan of applying Tier III requirements, the Energy Efficiency Design Index for new ships, and the Ship Energy Efficiency Management Plan for all ships. To assist policy formulation and follow-up, this study applies an energy consumption approach to estimate container ship emissions. The volumes of sulphur oxide (SOx), nitrous oxide (NOx), particulate matter (PM), and carbon dioxide (CO2) emitted from container ships are estimated using 2018 datasets on container shipping and average vessel speed records generated via AIS. Furthermore, the estimated reductions in SOx, NOx, PM, and CO2 are mapped for 2020. The empirical analysis demonstrates that the energy consumption approach is a valuable method to estimate ongoing emission reductions on a continuous basis and to fill data gaps where needed, as the latest worldwide container shipping emissions records date back to 2015. The presented analysis supports early-stage detection of environmental impacts in container shipping and helps to determine in which areas the greatest potential for emission reductions can be found

DETReg: Unsupervised Pretraining with Region Priors for Object Detection

Recent self-supervised pretraining methods for object detection largely focus on pretraining the backbone of the object detector, neglecting key parts of detection architecture. Instead, we introduce DETReg, a new self-supervised method that pretrains the entire object detection network, including the object localization and embedding components. During pretraining, DETReg predicts object localizations to match the localizations from an unsupervised region proposal generator and simultaneously aligns the corresponding feature embeddings with embeddings from a self-supervised image encoder. We implement DETReg using the DETR family of detectors and show that it improves over competitive baselines when finetuned on COCO, PASCAL VOC, and Airbus Ship benchmarks. In low-data regimes, including semi-supervised and few-shot learning settings, DETReg establishes many state-of-the-art results, e.g., on COCO we see a +6.0 AP improvement for 10-shot detection and over 2 AP improvements when training with only 1\% of the labels. For code and pretrained models, visit the project page at https://amirbar.net/detregComment: CVPR 2022 Camera Read