Boat Detection Research Articles

High-Resolution Sea Surface Target Detection Using Bi-Frequency High-Frequency Surface Wave Radar

The monitoring of the sea surface, whether it is the state of the sea or the position of targets (ships), is an up-to-date research topic. In order to determine localization parameters of ships, we propose a high-resolution algorithm for primary signal processing in high-frequency surface wave radar (HFSWR) which operates at two frequencies. The proposed algorithm is based on a high-resolution estimate of the range–Doppler (RD-HR) map formed at every antenna in the receive antenna array, which is an essential task, because the performance of the entire radar system depends on its estimation. We also propose a new focusing method allowing us to have only one RD-HR map in the detection process, which collects the information from both these carrier frequencies. The goal of the bi-frequency mode of operation is to improve the detectability of targets, because their signals are affected by different Bragg-line interference patterns at different frequencies, as seen on the RD-HR maps during the primary signal processing. Also, the effect of the sea (sea clutter) manifests itself in different ways at different frequencies. Some targets are masked (undetectable) at one frequency, but they become visible at another frequency. By exploiting this, we increase the probability of detection. The bi-frequency architecture (system model) for the localization of sea targets and the novel signal model are presented in this paper. The advantage of bi-frequency mode served as a motivation for testing the detectability of small boats, which is otherwise a very challenging task, primarily because such targets have a small radar reflective surface, they move quickly, and often change their direction. Based on experimentally obtained results, it can be observed that the probability of detection of small boats can also be significantly improved by using a bi-frequency architecture.

Adaptive spatial attention dual-branch fishing boat detection network

Adaptive spatial attention dual-branch fishing boat detection network

Night-Time Vessel Detection Based on Enhanced Dense Nested Attention Network

Efficient night-time vessel detection is of significant importance for maritime traffic management, fishery activity monitoring, and environmental protection. With the advancement in object-detection approaches, the method of night-time vessel detection has gradually shifted from traditional threshold segmentation to deep learning that balances efficiency and accuracy. However, the restricted spatial resolution of night-time light (NTL) remote sensing data (e.g., VIIRS/DNB images) results in fewer discernible features and insufficient training performance when detecting vessels that are considered small targets. To address this, we establish an Enhanced Dense Nested-Attention Network (DNA-net) to improve the detection of small vessel targets under low-light conditions. This approach effectively integrates the original VIIRS/DNB, spike median index (SMI), and spike height index (SHI) images to maintain deep-level features and enhance feature extraction. On this basis, we performed vessel detection based on the Enhanced DNA-net using VIIRS/DNB images of the Japan Sea, the South China Sea, and the Java Sea. It is noteworthy that the VIIRS Boat Detection (VBD) observations and the Automatic Identification System (AIS) data were cross-matched as the actual status of the vessels (VBD-AIS). The results show that the proposed Enhanced DNA-net achieves significant improvements in the evaluation metrics (e.g., IOU, Pd, Fa, and MPD) compared to the original DNA-net, achieving performance of 87.81%, 96.72%, 5.42%, and 0.36 Wpx, respectively. Meanwhile, we validated the detection performance of Enhanced DNA-net and strong VBD detection against VBD-AIS, showing that the Enhanced DNA-net achieves 1% better accuracy than strong VBD detection.

A new way to understand migration routes of oceanic squid (Ommastrephidae) from satellite data

AbstractVisible Infrared Imaging Radiometer Suite (VIIRS) Boat Detection (VBD) data have been widely used to study the patterns of fishing grounds and their linking to fishery targets, particularly species mainly caught by jiggers. In line with most species in the Ommastrephidae family, the population of Todarodes pacificus is made up of various splinter cohorts concerning the timing and location of hatching. Therefore, the satellite‐recorded fishing grounds consist of groups with complex age structures and different migration directions within cohorts. This study examined the age composition of harvestable stocks (age spectrum) of T. pacificus in the Japan Sea based on an early life history individual‐based model of T. pacificus and VBD data. Using the age spectrum, we analysed the relationship between fishery effort and the age of the target group. It was found that jiggers most prefer individuals around 310 ± 20 days. Furthermore, the correlation between ambient water temperature and fishing effort revealed that T. pacificus migrated to colder waters, reaching the coldest waters at 250 ± 7.5 days before moving back towards warmer waters. We discussed a possible way to use the age‐temperature relationship to analyse the flow of VBD distributions to record the movements related to the migration of the fishing target. The results show migration‐like trajectories, which are initially parallel to the isotherm, gradually deflect towards lower temperature sides over several months, sharply turn for about a month and then move back with a slight angle to the isotherms. The method provides a potential framework to improve our understanding of the active migration of oceanic squid.

Uncovering the Effects of the Southwest Monsoon on Fishing Activity in the Indian Ocean with VIIRS Boat Detection Data

Uncovering the Effects of the Southwest Monsoon on Fishing Activity in the Indian Ocean with VIIRS Boat Detection Data

Discovering Hidden Offshore Lighting Structures with Multiyear Low-Light Imaging Satellite Data

The NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) has a capability to detect lighting present at the earth’s surface. This low-light imaging capability was built to satisfy a requirement from meteorologists to detect clouds at night in the visible based on reflected moonlight. On land, human settlements are the dominant source of DNB detected surface lighting. Lights are also detected offshore primarily arising from fishing boats using lights to attract catch. In 2015, the Earth Observation Group developed the VIIRS Boat Detection (VBD) product and today several fishery agencies use VBD data to monitor fishing activity and compliance with closures. Recently EOG compiled the full record VBD detections as a 15 arc second global grid. The record spans 2012-2021 in Asia and 2017-2021 elsewhere. Upon reviewing the multiyear accumulation of VBD detections we were surprised to find a diversity of previously unseen lighting features [1]. These structures are not evident in VBD from single nights but are only revealed by accumulating the detections across a year or more. The additional features include lit platforms, transit lanes, and vessel anchorages associated with ports and passage straits. Another surprising finding is geopolitical divisions in the density of cumulative vessel detections. In this paper we provide a look into one of the densest areas of VBD detections in the world, from the Yellow Sea, East China Sea and Sea of Japan (East Sea).

Evolutionary algorithm for optimized CNN architecture search applied to real-time boat detection in aerial images

Evolutionary algorithm for optimized CNN architecture search applied to real-time boat detection in aerial images

Automated VIIRS Boat Detection Based on Machine Learning and Its Application to Monitoring Fisheries in the East China Sea

Remote sensing is essential for monitoring fisheries. Optical sensors such as the day–night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) have been a crucial tool for detecting vessels fishing at night. It remains challenging to ensure stable detections under various conditions affected by the clouds and the moon. Here, we develop a machine learning based algorithm to generate automatic and consistent vessel detection. As DNB data are large and highly imbalanced, we design a two-step approach to train our model. We evaluate its performance using independent vessel position data acquired from on-ship radar. We find that our algorithm demonstrates comparable performance to the existing VIIRS boat detection algorithms, suggesting its possible application to greater temporal and spatial scales. By applying our algorithm to the East China Sea as a case study, we reveal a recent increase in fishing activity by vessels using bright lights. Our VIIRS boat detection results aim to provide objective information for better stock assessment and management of fisheries.

THFE: A Triple-hierarchy Feature Enhancement method for tiny boat detection

In boat navigation, especially in complex sea conditions, the detection performance of the tiny boat is related to the safety of boat sailing. However, due to the tiny boat occupying fewer pixels, the effective features of the tiny boat are difficult to obtain. Current tiny object detection methods focus primarily on dataset size matching, feature fusion, and label assignment, lack of attention to texture and detail information loss, and insufficient semantic utilization. Here, we propose a Triple-hierarchy Feature Enhancement (THFE) method to detect tiny boats. The core idea behind THFE is to enhance the semantic information from different layers to supplement the effective features of tiny boats. It consists of three spaces: the super-resolution enhancement space, the semantic enhancement space, and the hierarchical enhancement space. In THFE, sub-pixel convolution, sparse self-attention mechanism, channel attention mechanism, and spatial attention mechanism are adopted to hierarchically enhance each layer’s high-level and low-level semantic features. Finally, each layer’s high-level and low-level semantic features are adaptively fused so that each feature map contains richer high-level and low-level semantic information. Experiments show that our proposed THFE method achieves impressive gains in detection performance. For example, in terms of AP50tiny, our method outperforms state-of-the-art methods by 1.7% on the TinyBoats dataset, 3.1% on the TinyPersons Dataset and 3.9% on the Tiny CityPersons Dataset. To further study the detection of tiny boats, we introduce a tiny boat dataset that will be publicly accessible.

Small Recreational Boat Detection Using Sentinel-1 Data for the Monitoring of Recreational Ecosystem Services

Recreational ecosystem services are crucial for human well-being, and nature-based recreational activities often support local economies. However, tourism is very often one of many threads that contribute to the environment, and, therefore, data regarding its spatial patterns are necessary for the long-term sustainable development of a region. The present study describes a method for the detection of small boats (<10 m in length) in lake conditions based on Sentinel-1 radar images. Our two-step algorithm uses adaptive thresholding and math morphology operators to extract boat detections. The algorithm was validated on 14 images of different types of lakes in the Great Masurian Lake District, Poland. The detection accuracy was 88.17%. We also assessed the spatial and temporal distribution of tourist traffic and compared satellite data to field data. The correlation between the satellite-based map and field observations was 0.76.

Nighttime fishing vessel observation in Bohai Sea based on VIIRS fishing vessel detection product (VBD)

Remote sensing of night light can be used to detect lights at night and identify the location of fishing boats at sea. Visible Infrared Imaging Radiometer (VIIRS) onboard NASA's SNPP (Suomi National Polar Partnership) satellite has remarkably improved the detection capabilities compared to previous satellites, therefore enabling the production of VBD (VIIRS Boat Detection) with better quality. In this study, we investigated the relationship between fishing activities and environmental factors using generalized additive models (GAMs). We utilized three spatial indexes: cluster index, heterogeneity index, and geographical center, to study the spatiotemporal distribution of fishing boats at night and their seasonal variation in the Bohai Sea based on the 12-months data in 2020. We observed that fishing activities fluctuated over time, with troughs in February and from May to August, which were respectively influenced by the lunar new year and summer fishing moratorium. At the end of the lunar new year and the moratorium, the fishing activities rebounded significantly. Returning boats from Shandong province caused a secondary peak in December. Spatially, detected fishing activities mostly took place in near-shore fishing grounds, such as Bohai Bay and Liaodong Bay, but were rare in central Bohai Sea. The convex hull of monthly geographical centers resembles a triangle. The shifting monthly geographical centers within the triangle was caused by the clustering of fishing boats in three fishing grounds: Liaodong Bay, Bohai Bay and Laizhou Bay, at different time. The results of this study can be used as a reference for the monitoring of fishery activities and fishery economic research in the Bohai Sea. The research methodology provides a low-cost way to track and monitor fishing vessels, therefore providing necessary information for decision making.

Variability of the thermal front and its relationship with Chlorophyll-a in the north Bay of Bengal

The spatial and temporal variability of the thermal fronts and their relationship with chlorophyll-a in the north Bay of Bengal (BOB) was studied using time series of MODIS daily Sea Surface Temperature (SST) and weekly chlorophyll-a concentration to investigate the locations of intense fish catch activities in the continental shelf and slope as inferred from NOAA Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime boat detection products. An edge-detection algorithm operated on each SST image to identify thermal fronts in which a gradient of 0.3 °C/pixel (∼0.075 °C/km) was chosen following the sensitivity analysis. These thermal fronts at the daily scale were aggregated weekly to estimate the thermal front density. A least square-based harmonic analysis procedure was adopted to estimate mean climatology and seasonal harmonics of thermal front density and chlorophyll-a data sets. It shows persistent and coherent spatial features between the datasets in their climatology and seasonal cycles. They exhibit distinct locations of maxima co-occurring between the 10 and 50 m isobaths in the continental shelf and slope region coincided with the areas of the VIIRS mapped fishing vessels cluster sites. The annual frequency dominates the seasonal cycles of TFD with a positive phase during November–March (winter and spring seasons) and peaks in January. Mean (residual) circulation, as simulated from a regional ocean model, shows coherent spatial features congruent to the persistent thermal fronts and steered by the bathymetry. These locations with a high probability of thermal fronts and high concentrations of Chlorophyll-a can be considered as the perennial potential fishing zone, with the winter season being a more favorable period for fishing.

Multiple Vessel Detection in Harsh Maritime Environments

Abstract Recently, research concerning the navigation of autonomous surface vehicles (ASVs) has been increasing. However, a large-scale implementation of these vessels is still held back by several challenges such as multi-object tracking. Attaining accurate object detection plays a big role in achieving successful tracking. This article presents the development of a detection model with an image-based Convolutional Neural Network trained through transfer learning, a deep learning technique. To train, test, and validate the detector module, data were collected with the SENSE ASV by sailing through two nearby ports, Leixões and Viana do Castelo, and recording video frames through its on-board cameras, along with a Light Detection And Ranging, GPS, and Inertial Measurement Unit data. Images were extracted from the collected data, composing a manually annotated dataset with nine classes of different vessels, along with data from other open-source maritime datasets. The developed model achieved a class mAP@[.5 .95] (mean average precision) of 89.5% and a clear improvement in boat detection compared to a multi-purposed state-of-the-art detector, YOLO-v4, with a 22.9% and 44.3% increase in the mAP with an Intersection over Union threshold of 50% and the mAP@[.5 .95], respectively. It was integrated in a detection and tracking system, being able to continuously detect nearby vessels and provide sufficient information for simple navigation tasks.

Boat Detection and Tracking Using RSSI Signal in A Device-Free Environment

Abstract: This project presents a device-free human detection method for using Received Signal Strength Indicator (RSSI) measurement of Wireless Sensor Network (WSN) with packet dropout based on ZigBee. Packet loss is observed to be a familiar phenomenon with transmissions of WSNs. The packet reception rate (PRR) based on a large number of data packets cannot reflect the real-time link quality accurately. So, it raises a real-time RSSI link quality evaluation method based on the exponential smoothing method. Then, a device-free human detection method is proposed. Compared to conventional solutions which utilize a complex set of sensors for detection, the proposed approach achieves the same only by RSSI volatility. The experimental measurements are conducted in laboratory. A high-quality network based on ZigBee is obtained, and then, RSSI can be calculated from the receiver sensor modules. Experimental results show the uncertainty of RSSI change at the moment of human through the network area and confirm the validity of the detection method. Keywords: RSSI Signal, Detection and tracking

Detection of Malicious Social Boats Using Learning Automata with URL Feature

Bots have made an impact on a variety of social media platforms. Twitter has been hit particularly hard, with bots accounting for a sizable amount of its user base. These bots have been used for nefarious purposes like distributing fake information about politicians and increasing celebrities' perceived popularity. These bots have the ability to alter the outcomes of standard social media analysis. Malicious social bots have also been employed to spread incorrect information (for example, emailing fraudulent urls), which can have real-world effects. To detect such hostile behaviors, the suggested systems employ machine learning methods such as Naive Bayes and RF.

Boat Detection in Marina Using Time-Delay Analysis and Deep Learning

An autonomous acoustic system based on two bottom-moored hydrophones, a two-input audio board and a small single-board computer was installed at the entrance of a marina to detect entering/exiting boat. Windowed time lagged cross-correlations are calculated by the system to find the consecutive time delays between the hydrophone signals and to compute a signal which is a function of the boats' angular trajectories. Since its installation, the single-board computer performs online prediction with a signal processing-based algorithm which achieved an accuracy of 80 %. To improve system performance, a convolutional neural network (CNN) is trained with the acquired data to perform real-time detection. Two classification tasks were considered (binary and multiclass) to both detect a boat and its direction of navigation. Finally, a trained CNN was implemented in a single-board computer to ensure that prediction can be performed in real time.

Automatic boat detection based on diffusion and radiation characterization of boat lights during night for VIIRS DNB imaging data.

Visible infrared imaging radiometer suite (VIIRS) day/night band (DNB) data has been used to detect lit boats during night as it is very sensitive to low radiances. The existing methods for boat detection from VIIRS DNB data are mainly based on thresholds that are estimated by the statistical characteristics of pixels or artificial experience. This may generate detection errors and poor adaptability due to the lack of characterization of boat lights. In this paper, a two-step threshold detection algorithm based on the point spread and the radiative characteristics of nightlight point sources is proposed, so that the interference from adjacent pixels could be reduced as much as possible and a reasonable threshold could be determined. Meanwhile, this algorithm is applied to three study areas, namely the sea area around Tianjin Port in Bohai Sea, the sea area around Shanghai Port in East China Sea, and the sea area around Port Sulphur in Gulf of Mexico. It is demonstrated that the detection precision of the proposed algorithm reaches up to 90% and the recall rate reaches up to 85% in three areas when validated by visual interpretation, and the precision is 85.71% when validated by automatic identification system (AIS) data in the study area of the sea area around Port Sulphur in Gulf of Mexico, which approximately increases by 5% compared with the previous algorithm.

Directional processing in assessment of wind turbine noise

Assessments of wind turbine generator (WTG) noise are required to comply with the US Environmental Agency and local governments and avoid legal action that may result of non-compliant operation. Current methods for WTG noise measurements require the comparison of long-term sound data recorded before and after a WTG installation. These measurements must be conducted during several months for various wind speeds and environmental conditions. Such measurements are complicated due to the presence of multiple sources other than noise not from WTG, including intermittent and fluctuating sources. The application of directional processing can help localize and identify the sources responsible for increased sound levels at any given moment and to estimate the contribution of the other than WTG sources to the whole sound level. Stevens has developed various acoustic systems with several sensors that provide directional processing of sounds that were previously applied for detection, tracking and classification of small boats, divers, small aircraft, and drones. We propose modifications of such systems for directional sensing of WTG noise at long distances in the frequency band relevant for WTG noise measurement.

Ghana’s Oil find and the Threat of Maritime Piracy in the Shipping Corridor of the Gulf of Guinea

The Gulf of Guinea (GoG) has now carved a name for itself as a piracy hotspot now surpassing the Gulf of Aden in pirate attacks. This sea corridor remains a life line to many of the littoral and land locked states in that region. Oil is an important commodity and the sea lines of communications cannot be overemphasized. Ghana joined the main players of oil production when oil was discovered its Jubilee Oil Field. Being an offshore venture, the oil is stored in the floating production storage and offloading (FPSO) vessel and later offloaded on sea tanker vessels for export. The FPSO is visible as a stationary vessel a couple of nautical miles from the shore and therefore makes it vulnerable to piracy attacks. This paper highlights the modus operandi of these pirate groups notable among them are the Movement for the Emancipation of the Niger Delta (MEND) and also in 2016 the emergence of the Niger Delta Avengers. A PESTLE and SWOT analysis of the situation is looked at while some of the drivers mitigating the ascendency of piracy in this region is mentioned. There is the need for the regional and international security agencies to coordinate their efforts and intensify their efforts in policing the GoG. As a means of Best Management Practice (BMP), vessels are advised to ensure adherence to these practices and fortify themselves with modern communication devices and surveillance equipment to help make early detection of pirate boats as well as having a well-equipped citadel with dedicated communication devices should the need arise to use them.

Assessing Potential Climatic and Human Pressures in Indonesian Coastal Ecosystems Using a Spatial Data-Driven Approach

Blue carbon ecosystems are key for successful global climate change mitigation; however, they are one of the most threatened ecosystems on Earth. Thus, this study mapped the climatic and human pressures on the blue carbon ecosystems in Indonesia using multi-source spatial datasets. Data on moderate resolution imaging spectroradiometer (MODIS) ocean color standard mapped images, VIIRS (visible, infrared imaging radiometer suite) boat detection (VBD), global artificial impervious area (GAIA), MODIS surface reflectance (MOD09GA), MODIS land surface temperature (MOD11A2), and MODIS vegetation indices (MOD13A2) were combined using remote sensing and spatial analysis techniques to identify potential stresses. La Niña and El Niño phenomena caused sea surface temperature deviations to reach −0.5 to +1.2 °C. In contrast, chlorophyll-a deviations reached 22,121 to +0.5 mg m−3. Regarding fishing activities, most areas were under exploitation and relatively sustained. Concerning land activities, mangrove deforestation occurred in 560.69 km2 of the area during 2007–2016, as confirmed by a decrease of 84.9% in risk-screening environmental indicators. Overall, the potential pressures on Indonesia’s blue carbon ecosystems are varied geographically. The framework of this study can be efficiently adopted to support coastal and small islands zonation planning, conservation prioritization, and marine fisheries enhancement.