Model pruning in depth completion CNNs for forestry robotics with simulated annealing

In this article, we present an analysis of model compression in depth completion neural networks for forestry robotics, considering the increasing demands of real time autonomous solutions. Specifically, we implement a single state simulated annealing meta-heuristic for model pruning in the ENet and MSG-CHN neural networks for depth completion. We run experiments in three different datasets and analyze how different levels of pruning affect the accuracy and speed of the models. Experimental tests show that increasing sparsity has different effects depending on the neural network and dataset. ENet has negligible difference in accuracy and it would greatly benefit from lowering the amount of FLOPs, while MSG-CHN displays an inconsistent behavior depending on the dataset. This suggests that while both models benefit from model compression techniques, the optimal sparsity level depends on environment, dataset and neural network

Generating synthetic multispectral images for semantic segmentation in forestry applications

In this paper, we introduce a GAN-based solution for generating synthetic multispectral images from fully-annotated RGB images for data augmentation purposes in forestry robotics applications at ground-level. Fully-annotated multispectral datasets are difficult to obtain with sufficient training samples when compared to RGB-based datasets, since annotation in this case is often very time-consuming and expensive due to the need for expert knowledge. In this text, a study comparing different GAN-based image translation models designed for data augmentation is presented. Synthetic images generated by the proposed solution are shown to be realistic enough to yield performance ratings comparable to what is obtained using real images

Testing different CNN architectures for semantic segmentation for landscaping with forestry robotics

Increasingly mechanized, leading to the emergence of forestry robotics. In this article, we present the results of our evaluation of a set of state-of-the-art convolutional neural network-based solutions for semantic segmentation using the Bonnetal open-source training and deployment framework, together with a custom-made solution based on an adaptation of an alternative decoder and encoder for that framework, the Adapnet++–eASPP architecture, in the context of a robotic perception pipeline designed to perform landscaping in woodlands to reduce the amount of living flammable material (the Fuel class) for wildfire prevention. Results show that, overall, Adapnet++–eASPP was the most robust and comprehensive encoder for our application, demonstrating a consistently high average level of performance in comparison to the other architectures, and displaying the greatest robustness of the group. With this solution, we demonstrated to be able to satisfy our requirements of a low rate of false positives for the Fuel class and operational performance of 10fps

Arrival Directions of Cosmic Rays above 32 EeV from Phase One of the Pierre Auger Observatory

A promising energy range to look for angular correlations between cosmic rays of extragalactic origin and their sources is at the highest energies, above a few tens of EeV (1 EeV equivalent to 10^(18) eV). Despite the flux of these particles being extremely low, the area of similar to 3000 km^(2) covered at the Pierre Auger Observatory, and the 17 yr data-taking period of the Phase 1 of its operations, have enabled us to measure the arrival directions of more than 2600 ultra-high-energy cosmic rays above 32 EeV. We publish this data set, the largest available at such energies from an integrated exposure of 122,000 km^(2) sr yr, and search it for anisotropies over the 3.4 pi steradians covered with the Observatory. Evidence for a deviation in excess of isotropy at intermediate angular scales, with similar to 15 degrees Gaussian spread or similar to 25 degrees top-hat radius, is obtained at the 4 sigma significance level for cosmic-ray energies above similar to 40 EeV

Search for Ultra-high-energy Photons from Gravitational Wave Sources with the Pierre Auger Observatory

A search for time-directional coincidences of ultra-high-energy (UHE) photons above 10 EeV with gravitational wave (GW) events from the LIGO/Virgo runs O1 to O3 is conducted with the Pierre Auger Observatory. Due to the distinctive properties of photon interactions and to the background expected from hadronic showers, a subset of the most interesting GW events is selected based on their localization quality and distance. Time periods of 1000 s around and 1 day after the GW events are analyzed. No coincidences are observed. Upper limits on the UHE photon fluence from a GW event are derived that are typically at & SIM;7 MeV cm(-2) (time period 1000 s) and & SIM;35 MeV cm(-2) (time period 1 day). Due to the proximity of the binary neutron star merger GW170817, the energy of the source transferred into UHE photons above 40 EeV is constrained to be less than 20% of its total GW energy. These are the first limits on UHE photons from GW sources

Searches for Ultra-High-Energy Photons at the Pierre Auger Observatory

The Pierre Auger Observatory, which is the largest air-shower experiment in the world, offers unprecedented exposure to neutral particles at the highest energies. Since the start of data collection more than 18 years ago, various searches for ultra-high-energy (UHE, E greater than or similar to 10^(17) eV) photons have been performed, either for a diffuse flux of UHE photons, for point sources of UHE photons or for UHE photons associated with transient events such as gravitational wave events. In the present paper, we summarize these searches and review the current results obtained using the wealth of data collected by the Pierre Auger Observatory

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

SEMFIRE forest dataset for semantic segmentation and data augmentation

These datasets are used for semantic segmentation and data augmentation and contain various forestry scenes. They were collected as part of the research work conducted by the Institute of Systems and Robotics, University of Coimbra team within the scope of the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. CENTRO-01-0247-FEDER-032691) research project coordinated by Ingeniarius Ltd. The semantic segmentation algorithms attempt to identify various semantic classes (e.g. background, live flammable materials, trunks, canopies etc.) in the images of the datasets. The datasets include diverse image types, e.g. original camera images and their labeled images. In total the SEMFIRE datasets include about 1700 image pairs. Each dataset includes corresponding .bag files