Are object detection assessment criteria ready for maritime computer vision?

Maritime vessels equipped with visible and infrared cameras can complement other conventional sensors for object detection. However, application of computer vision techniques in maritime domain received attention only recently. The maritime environment offers its own unique requirements and challenges. Assessment of the quality of detections is a fundamental need in computer vision. However, the conventional assessment metrics suitable for usual object detection are deficient in the maritime setting. Thus, a large body of related work in computer vision appears inapplicable to the maritime setting at the first sight. We discuss the problem of defining assessment metrics suitable for maritime computer vision. We consider new bottom edge proximity metrics as assessment metrics for maritime computer vision. These metrics indicate that existing computer vision approaches are indeed promising for maritime computer vision and can play a foundational role in the emerging field of maritime computer vision

Numerical simulation of clouds and precipitation depending on different relationships between aerosol and cloud droplet spectral dispersion

The aerosol effects on clouds and precipitation in deep convective cloud systems are investigated using the Weather Research and Forecast (WRF) model with the Morrison two-moment bulk microphysics scheme. Considering positive or negative relationships between the cloud droplet number concentration (Nc) and spectral dispersion (ɛ), a suite of sensitivity experiments are performed using an initial sounding data of the deep convective cloud system on 31 March 2005 in Beijing under either a maritime (‘clean’) or continental (‘polluted’) background. Numerical experiments in this study indicate that the sign of the surface precipitation response induced by aerosols is dependent on the ɛ−Nc relationships, which can influence the autoconversion processes from cloud droplets to rain drops. When the spectral dispersion ɛ is an increasing function of Nc, the domain-average cumulative precipitation increases with aerosol concentrations from maritime to continental background. That may be because the existence of large-sized rain drops can increase precipitation at high aerosol concentration. However, the surface precipitation is reduced with increasing concentrations of aerosol particles when ɛ is a decreasing function of Nc. For the ɛ−Nc negative relationships, smaller spectral dispersion suppresses the autoconversion processes, reduces the rain water content and eventually decreases the surface precipitation under polluted conditions. Although differences in the surface precipitation between polluted and clean backgrounds are small for all the ɛ−Nc relationships, additional simulations show that our findings are robust to small perturbations in the initial thermal conditions. Keywords: aerosol indirect effects, cloud droplet spectral dispersion, autoconversion parameterization, deep convective systems, two-moment bulk microphysics schem

Multi-scale modeling study of the source contributions to near-surface ozone and sulfur oxides levels over California during the ARCTAS-CARB period

Chronic high surface ozone (O_3) levels and the increasing sulfur oxides (SO_x = SO_2 + SO_4) ambient concentrations over South Coast (SC) and other areas of California (CA) are affected by both local emissions and long-range transport. In this paper, multi-scale tracer, full-chemistry and adjoint simulations using the STEM atmospheric chemistry model are conducted to assess the contribution of local emission sourcesto SC O_3 and to evaluate the impacts of transported sulfur and local emissions on the SC sulfur budgetduring the ARCTAS-CARB experiment period in 2008. Sensitivity simulations quantify contributions of biogenic and fire emissions to SC O_3 levels. California biogenic and fire emissions contribute 3–4 ppb to near-surface O_3 over SC, with larger contributions to other regions in CA. During a long-range transport event from Asia starting from 22 June, high SO_x levels (up to ~0.7 ppb of SO_2 and ~1.3 ppb of SO_4) is observed above ~6 km, but they did not affect CA surface air quality. The elevated SO_x observed at 1–4 km is estimated to enhance surface SO_x over SC by ~0.25 ppb (upper limit) on ~24 June. The near-surface SO_x levels over SC during the flight week are attributed mostly to local emissions. Two anthropogenic SO_x emission inventories (EIs) from the California Air Resources Board (CARB) and the US Environmental Protection Agency (EPA) are compared and applied in 60 km and 12 km chemical transport simulations, and the results are compared withobservations. The CARB EI shows improvements over the National Emission Inventory (NEI) by EPA, but generally underestimates surface SC SO_x by about a factor of two. Adjoint sensitivity analysis indicated that SO_2 levels at 00:00 UTC (17:00 local time) at six SC surface sites were influenced by previous day maritime emissions over the ocean, the terrestrial emissions over nearby urban areas, and by transported SO_2 from the north through both terrestrial and maritime areas. Overall maritime emissions contribute 10–70% of SO2 and 20–60% fine SO_4 on-shore and over the most terrestrial areas, with contributions decreasing with in-land distance from the coast. Maritime emissions also modify the photochemical environment, shifting O_3 production over coastal SC to more VOC-limited conditions. These suggest an important role for shipping emission controls in reducing fine particle and O_3 concentrations in SC

Multi-scale modeling study of the source contributions to near-surface ozone and sulfur oxides levels over California during the ARCTAS-CARB period

Chronic high surface ozone (O3) levels and the increasing sulfur oxides (SOx = SO2+SO4) ambient concentrations over South Coast (SC) and other areas of California (CA) are affected by both local emissions and long-range transport. In this paper, multi-scale tracer, full-chemistry and adjoint simulations using the STEM atmospheric chemistry model are conducted to assess the contribution of local emission sourcesto SC O3 and to evaluate the impacts of transported sulfur and local emissions on the SC sulfur budgetduring the ARCTAS-CARB experiment period in 2008. Sensitivity simulations quantify contributions of biogenic and fire emissions to SC O3 levels. California biogenic and fire emissions contribute 3–4 ppb to near-surface O3 over SC, with larger contributions to other regions in CA. During a long-range transport event from Asia starting from 22 June, high SOx levels (up to ~0.7 ppb of SO2 and ~1.3 ppb of SO4) is observed above ~6 km, but they did not affect CA surface air quality. The elevated SOx observed at 1–4 km is estimated to enhance surface SOx over SC by ~0.25 ppb (upper limit) on ~24 June. The near-surface SOx levels over SC during the flight week are attributed mostly to local emissions. Two anthropogenic SOx emission inventories (EIs) from the California Air Resources Board (CARB) and the US Environmental Protection Agency (EPA) are compared and applied in 60 km and 12 km chemical transport simulations, and the results are compared withobservations. The CARB EI shows improvements over the National Emission Inventory (NEI) by EPA, but generally underestimates surface SC SOx by about a factor of two. Adjoint sensitivity analysis indicated that SO2 levels at 00:00 UTC (17:00 local time) at six SC surface sites were influenced by previous day maritime emissions over the ocean, the terrestrial emissions over nearby urban areas, and by transported SO2 from the north through both terrestrial and maritime areas. Overall maritime emissions contribute 10–70% of SO2 and 20–60% fine SO4 on-shore and over the most terrestrial areas, with contributions decreasing with in-land distance from the coast. Maritime emissions also modify the photochemical environment, shifting O3 production over coastal SC to more VOC-limited conditions. These suggest an important role for shipping emission controls in reducing fine particle and O3concentrations in SC

Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results

Challenges in video based object detection in maritime scenario using computer vision

This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here

Automatic detection, tracking and counting of birds in marine video content

Robust automatic detection of moving objects in a marine context is a multi-faceted problem due to the complexity of the observed scene. The dynamic nature of the sea caused by waves, boat wakes, and weather conditions poses huge challenges for the development of a stable background model. Moreover, camera motion, reflections, lightning and illumination changes may contribute to false detections. Dynamic background subtraction (DBGS) is widely considered as a solution to tackle this issue in the scope of vessel detection for maritime traffic analysis. In this paper, the DBGS techniques suggested for ships are investigated and optimized for the monitoring and tracking of birds in marine video content. In addition to background subtraction, foreground candidates are filtered by a classifier based on their feature descriptors in order to remove non-bird objects. Different types of classifiers have been evaluated and results on a ground truth labeled dataset of challenging video fragments show similar levels of precision and recall of about 95% for the best performing classifier. The remaining foreground items are counted and birds are tracked along the video sequence using spatio-temporal motion prediction. This allows marine scientists to study the presence and behavior of birds

Correction of upstream flow and hydraulic state with data assimilation in the context of flood forecasting

The present study describes the assimilation of river water level observations and the resulting improvement in flood forecasting. The Kalman Filter algorithm was built on top of a one-dimensional hydraulic model which describes the Saint-Venant equations. The assimilation algorithm folds in two steps: the first one was based on the assumption that the upstream flow can be adjusted using a three-parameter correction; the second one consisted of directly correcting the hydraulic state. This procedure was applied using a four- day sliding window over the flood event. The background error covariances for water level and discharge were repre- sented with anisotropic correlation functions where the cor- relation length upstream of the observation points is larger than the correlation length downstream of the observation points. This approach was motivated by the implementation of a Kalman Filter algorithm on top of a diffusive flood wave propagation model. The study was carried out on the Adour and the Marne Vallage (France) catchments. The correction of the upstream flow as well as the control of the hydraulic state during the flood event leads to a significant improve- ment in the water level and discharge in both analysis and forecast modes