A new paradigm for estimating the prevalence of plastic litter in the marine environment.

The intelligent method proposed herein is formulated on a deep learning technique which can identify, localise and map the shape of plastic debris in the marine environment. Utilising images depicting plastic litter from six beaches in Cyprus, the developed tool pointed to a plastic litter density of 0.035 items/m2. Extrapolated to the entire shorelines of the island, the intelligent approach estimated about 66,000 plastic articles weighting a total of ≈1000 kg. Besides deducing the plastic litter density, the dimensions of all documented plastic litter were determined with the aid of the OpenCV Contours image processing tool. Results revealed that the dominant object length ranged between 10 and 30 cm which is in agreement with the length of common plastic litter often spoiling these coastlines. Concluding, only in-situ visual scan sample surveys and no manual collection means were used to predict the density and the dimensions of the plastic litter.

An intelligent way for discerning plastics at the shorelines and the seas.

Irrespective of how plastics litter the coastline or enter the sea, they pose a major threat to birds and marine life alike. In this study, an artificial intelligence tool was used to create an image classifier based on a convolutional neural network architecture that utilises the bottleneck method. The trained bottleneck method classifier was able to categorise plastics encountered either at the shoreline or floating at the sea surface into eight distinct classes, namely, plastic bags, bottles, buckets, food wrappings, straws, derelict nets, fish, and other objects. Discerning objects with a success rate of 90%, the proposed deep learning approach constitutes a leap towards the smart identification of plastics at the coastline and the sea. Training and testing loss and accuracy results for a range of epochs and batch sizes have lent credibility to the proposed method. Results originating from a resolution sensitivity analysis demonstrated that the prediction technique retains its ability to correctly identify plastics even when image resolution was downsized by 75%. Intelligent tools, such as the one suggested here, can replace manual sorting of macroplastics from human operators revealing, for the first time, the true scale of the amount of plastic polluting our beaches and the seas.

Efficient Evaluation of Image Quality via Deep-Learning Approximation of Perceptual Metrics.

Image metrics based on Human Visual System (HVS) play a remarkable role in the evaluation of complex image processing algorithms. However, mimicking the HVS is known to be complex and computationally expensive (both in terms of time and memory), and its usage is thus limited to a few applications and to small input data. All of this makes such metrics not fully attractive in real-world scenarios. To address these issues, we propose Deep Image Quality Metric (DIQM), a deep-learning approach to learn the global image quality feature (mean-opinion-score). DIQM can emulate existing visual metrics efficiently, reducing the computational costs by more than an order of magnitude with respect to existing implementations.

Identifying floating plastic marine debris using a deep learning approach.

Estimating the volume of macro-plastics which dot the world's oceans is one of the most pressing environmental concerns of our time. Prevailing methods for determining the amount of floating plastic debris, usually conducted manually, are time demanding and rather limited in coverage. With the aid of deep learning, herein, we propose a fast, scalable, and potentially cost-effective method for automatically identifying floating marine plastics. When trained on three categories of plastic marine litter, that is, bottles, buckets, and straws, the classifier was able to successfully recognize the preceding floating objects at a success rate of ≈ 86%. Apparently, the high level of accuracy and efficiency of the developed machine learning tool constitutes a leap towards unraveling the true scale of floating plastics.

A Gamut-Mapping Framework for Color-Accurate Reproduction of HDR Images.

Few tone mapping operators (TMOs) take color management into consideration, limiting compression to luminance values only. This could lead to changes in image chroma and hues, which are typically managed with a post-processing step. However, current post-processing techniques for tone reproduction do not explicitly consider the target display gamut. Gamut mapping, on the other hand, deals with mapping images from one color gamut to another, usually smaller, gamut but has traditionally focused on smaller scale, chromatic changes. The authors present a combined gamut- and tone-management framework for color-accurate reproduction of high dynamic range images that can prevent hue and luminance shifts while taking gamut boundaries into consideration. Their approach is conceptually and computationally simple, parameter-free, and compatible with existing TMOs.

Radial basis function networks GPU-based implementation.

Neural networks (NNs) have been used in several areas, showing their potential but also their limitations. One of the main limitations is the long time required for the training process; this is not useful in the case of a fast training process being required to respond to changes in the application domain. A possible way to accelerate the learning process of an NN is to implement it in hardware, but due to the high cost and the reduced flexibility of the original central processing unit (CPU) implementation, this solution is often not chosen. Recently, the power of the graphic processing unit (GPU), on the market, has increased and it has started to be used in many applications. In particular, a kind of NN named radial basis function network (RBFN) has been used extensively, proving its power. However, their limiting time performances reduce their application in many areas. In this brief paper, we describe a GPU implementation of the entire learning process of an RBFN showing the ability to reduce the computational cost by about two orders of magnitude with respect to its CPU implementation.