ShorelineNet: An Efficient Deep Learning Approach for Shoreline Semantic Segmentation for Unmanned Surface Vehicles

This paper introduces a novel deep learning approach to semantic segmentation of the shoreline environments with a high frames-per-second (fps) performance, making the approach readily applicable to autonomous navigation for Unmanned Surface Vehicles (USV). The proposed ShorelineNet is an efficient deep neural network of high performance relying only on visual input. ShorelineNet uses monocular visual input to produce accurate shoreline separation and obstacle detection compared to the state-of-the-art, and achieves this with realtime performance. Experimental validation on a challenging multi-modal maritime obstacle detection dataset, the MODD2 dataset, achieves a much faster inference (25fps on an NVIDIA Tesla K80 and 6fps on a CPU) with respect to the recent state-of-the-art methods, while keeping the performance equally high (73.1% F-score). This makes ShorelineNet a robust and effective model to be used for reliable USV navigation that require real-time and high-performance semantic segmentation of maritime environments

Diseño de algoritmo realimentado para detección de objetos en entornos continuos

En el presente artículo se describe el Mask Feedback Algorithm (MFA) desarrollado en el Laboratorio de Sistemas Distribuidos (LSD) de la Facultad de Ingeniería de la Universidad Nacional de Asunción (FIUNA), con el propósito de detectar objetos presentes en superficies acuáticas desarrollado como una de las componentes fundamentales del proyecto “Surface Drone for the Study of Water Quality” también perteneciente al LSD, sin embargo MFA puede ser adaptado a un mayor número de situaciones en las cuales está presente un entorno continuo y se pretende extraer información referente a objetos sumergidos parcialmente en dicho entorno.CONACYT – Consejo Nacional de Ciencia y TecnologíaPROCIENCI

Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles

We address the problem of optical decalibration in mobile stereo camera setups, especially in context of autonomous vehicles. In real world conditions, an optical system is subject to various sources of anticipated and unanticipated mechanical stress (vibration, rough handling, collisions). Mechanical stress changes the geometry between the cameras that make up the stereo pair, and as a consequence, the pre-calculated epipolar geometry is no longer valid. Our method is based on optimization of camera geometry parameters and plugs directly into the output of the stereo matching algorithm. Therefore, it is able to recover calibration parameters on image pairs obtained from a decalibrated stereo system with minimal use of additional computing resources. The number of successfully recovered depth pixels is used as an objective function, which we aim to maximize. Our simulation confirms that the method can run constantly in parallel to stereo estimation and thus help keep the system calibrated in real time. Results confirm that the method is able to recalibrate all the parameters except for the baseline distance, which scales the absolute depth readings. However, that scaling factor could be uniquely determined using any kind of absolute range finding methods (e.g. a single beam time-of-flight sensor).Comment: 8 pages, 9 figure

Object detection and classification in aquatic environment using convolutional neural networks

We address the problem of real-time floating obstacle detection in aquatic environments with convolutional neural networks. Reliable and fast detection of obstacles is crucial for autonomous driving. Convolutional neural networks are often used in autonomous cars but have not yet been thoroughly tested in the aquatic environment. For this purpose, we analyze two of the latest convolutional neural networks for object detection and classification: YOLO and BlitzNet. We propose a modified convolutional neural network for obstacle detection YoloW and a new dataset for object detection in the aquatic environment. The dataset contains 19691 annotated obstacles appearing in 12168 images. We propose a customized learning process from uncertain training examples, which is suitable for training convolutional neural networks on real world datasets. We evaluate the performance of the presented convolutional neural networks on the proposed dataset. By increasing the number of training examples, the accuracy of all models is improved. After training on the entire training set of our dataset, BlitzNet achieves an average accuracy of 89.68%, YOLO 96.78%, while our model YoloW achieves an average accuracy of 97.72%. The proposed YoloW works in real-time and is capable of obstacle detection at 30.12 images per second on average