Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System

none5During the last decade vessel-position-recording devices, such as the Vessel Monitoring System and the Automatic Identification System, have increasingly given accurate spatial and quantitative information of industrial fisheries. On the other hand, small-scale fisheries (vessels below 12 m) remain untracked and largely unregulated even though they play an important socio-economic and cultural role in European waters and coastal communities and account for most of the total EU fishing fleet. The typically low-technological capacity of these small-scale fishing boats—for which space and power onboard are often limited—as well their reduced operative range encourage the development of efficient, low-cost, and low-burden tracking solutions. In this context, we designed a cost-effective and scalable prototypic architecture to gather and process positional data from small-scale vessels, making use of a LoRaWAN/cellular network. Data collected by our first installation are presented, as well as its preliminary processing. The emergence of a such low-cost and open-source technology coupled to artificial intelligence could open new opportunities for equipping small-scale vessels, collecting their trajectory data, and estimating their fishing effort (information which has historically not been present). It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to inform coastal resource and fisheries management.openAnna Nora Tassetti, Alessandro Galdelli, Jacopo Pulcinella, Adriano Mancini, Luca BologniniNora Tassetti, Anna; Galdelli, Alessandro; Pulcinella, Jacopo; Mancini, Adriano; Bolognini, Luc

Using AIS to Attempt a Quantitative Evaluation of Unobserved Trawling Activity in the Mediterranean Sea

In the past decades, the Automatic Identification System (AIS) has been employed in numerous research fields as a valuable tool for, among other things, Maritime Domain Awareness and Maritime Spatial Planning. In contrast, its use in fisheries management is hampered by coverage and transmission gaps. Transmission gaps may be due to technical limitations (e.g., weak signal or interference with other signals) or to deliberate switching off of the system, to conceal fishing activities. In either case such gaps may result in underestimating fishing effort and pressure. This study was undertaken to map and analyze bottom trawler transmission gaps in terms of duration and distance from the harbor with a view to quantifying unobserved fishing and its effects on overall trawling pressure. Here we present the first map of bottom trawler AIS transmission gaps in the Mediterranean Sea and a revised estimate of fishing effort if some gaps are considered as actual fishing

A Novel Remote Visual Inspection System for Bridge Predictive Maintenance

Predictive maintenance on infrastructures is currently a hot topic. Its importance is proportional to the damages resulting from the collapse of the infrastructure. Bridges, dams and tunnels are placed on top on the scale of severity of potential damages due to the fact that they can cause loss of lives. Traditional inspection methods are not objective, tied to the inspector’s experience and require human presence on site. To overpass the limits of the current technologies and methods, the authors of this paper developed a unique new concept: a remote visual inspection system to perform predictive maintenance on infrastructures such as bridges. This is based on the fusion between advanced robotic technologies and the Automated Visual Inspection that guarantees objective results, high-level of safety and low processing time of the results

Benchmarking of Dual-Step Neural Networks for Detection of Dangerous Weapons on Edge Devices

Nowadays, criminal activities involving hand-held weapons are widespread throughout the world and pose a significant problem for the community. The development of Video Surveillance Systems (VSV) and Artificial Intelligence (AI) approaches have made it possible to implement automatic systems for detecting dangerous weapons even in crowded environments. However, the detection of hand-held weapons - usually very small in size with respect to the Field of View (FoV) of the camera - is still an open challenge. The use of complex hardware systems and deep learning (DL) architectures have mitigated this problem and achieved excellent results, but involve high costs and high performance that hinder the deployment of such systems. In this contest, we present a comprehensive performance comparison in terms of inference time and detection accuracy of two low-cost edge devices: Google Coral Dev board and NVIDIA Jetson Nano. We deployed and run on both boards a dual-step DL framework for hand-held weapons detection exploiting half-precision floating-point (FP16) quantization on Jetson Nano and 8-bit signed integer (INT8) quantization on Coral Dev. Our results show that both in terms of PASCAL VOC mean Average Precision (mAP) and Frames per Second (FPS), the framework running on Jetson Nano (mAP = 99.6, FPS = 4.5, 2.5, 1.7, 1.4 from 1 to 4 people in the camera FoV, respectively) slightly outperform the Coral's one (mAP = 98.8 and FPS = 2.9. 1.5, 1.1, 0.9 from 1 to 4 people in the camera FoV, respectively). The Coral Dev obtained the highest inference speed (FPS = 36.5) overcoming the Jetson Nano (FPS=23.8) only when running the dual-step framework with no people in the camera FoV. In conclusion, the benchmark on the two edge devices points out that both allow to run the framework with satisfactory results, pushing towards the diffusion of such on-the-edge systems in a real-world scenario

A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities

Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of suspicious behaviors in close proximity of managed areas. The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight “suspicious” AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel—and the gear it adopts—is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not). Results allow the discrimination of collaborative and non-collaborative ships, playing a key role in detecting potential suspect behaviors especially in close proximity of managed areas

A low-cost and low-burden secure solution to track small-scale fisheries

During the last decade accurate spatial and quantitative information of industrial fisheries have been increasingly given using tracking technologies and machine learning analytical algorithms. However, in most small-scale fisheries, lack of spatial data has been a recurrent bottleneck as Vessel Monitoring System and Automatic Identification System, developed for vessels longer than 12 and 15 m in length respectively, have little applicability in these contexts. It follows that small-scale vessels (< 12 m in length) remain untracked and largely unregulated, even though they account for most of the fishing fleet in operation in the Mediterranean Sea. As such, the tracking of small-scale fleets tends to require the use of novel and low cost solutions that could be addressed by small vessels often without dedicated electrical systems. In this paper we propose a scalable architecture that makes use of a low-cost LoRaWAN/cellular network to acquire and process positioning data from small-scale vessels; preliminary results of a first installation of the prototype are presented, as well as the data collected. The emergence of a such low-cost and open source technology coupled to artificial intelligence could open new opportunities for equipping small-scale vessels, collecting their trajectory data and estimating their fishing effort (information which has historically not been present). It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to inform coastal resource and fisheries management, and cross-border marine spatial planning

Underwater mussel culture grounds: precision technologies for management purposes

The exploitation of offshore mussel farms is becoming relevant almost everywhere, even though it lags way behind other food sectors that are already supported by mature monitoring, modeling, prediction, and analysis tools. New technologies and sensors could indeed boost this sector and alleviate key challenges facing the aquaculture industry. However, experiences and usable solutions are still scarce. Here, we propose a first attempt to introduce Augmented Reality (AR) technologies - compositing real environments and simulated objects - to increase the human perception of a scene in real time and support the management of a mussel culture ground. The case study offshore farm, located in the central Adriatic Sea, is structured in submerged mussel long-lines and, partly, in innovative submerged fixed poles. Object detection in video with deep learning is used to geo-locate buoys in real time, while a multibeam echosounder allowed geo-referenced observations of the fixed mussel poles not otherwise visible by the onboard camera. The AR prototype supports farmers to accomplish their every-day offshore tasks and have real-time visual access to the whole farm and related geographical information system