Data Flow ORB-SLAM for Real-time Performance on Embedded GPU Boards

The use of embedded boards on robots, including unmanned aerial and ground vehicles, is increasing thanks to the availability of GPU equipped low-cost embedded boards in the market. Porting algorithms originally designed for desktop CPUs on those boards is not straightforward due to hardware limitations. In this paper, we present how we modified and customized the open source SLAM algorithm ORB-SLAM2 to run in real-time on the NVIDIA Jetson TX2. We adopted a data flow paradigm to process the images, obtaining an efficient CPU/GPU load distribution that results in a processing speed of about 30 frames per second. Quantitative experimental results on four different sequences of the KITTI datasets demonstrate the effectiveness of the proposed approach. The source code of our data flow ORB-SLAM2 algorithm is publicly available on GitHub

Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming

An effective perception system is a fundamental component for farming robots, as it enables them to properly perceive the surrounding environment and to carry out targeted operations. The most recent approaches make use of state-of-the-art machine learning techniques to learn an effective model for the target task. However, those methods need a large amount of labelled data for training. A recent approach to deal with this issue is data augmentation through Generative Adversarial Networks (GANs), where entire synthetic scenes are added to the training data, thus enlarging and diversifying their informative content. In this work, we propose an alternative solution with respect to the common data augmentation techniques, applying it to the fundamental problem of crop/weed segmentation in precision farming. Starting from real images, we create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with their synthesized counterparts. To do that, we employ a conditional GAN (cGAN), where the generative model is trained by conditioning the shape of the generated object. Moreover, in addition to RGB data, we take into account also near-infrared (NIR) information, generating four channel multi-spectral synthetic images. Quantitative experiments, carried out on three publicly available datasets, show that (i) our model is capable of generating realistic multi-spectral images of plants and (ii) the usage of such synthetic images in the training process improves the segmentation performance of state-of-the-art semantic segmentation Convolutional Networks.Comment: Submitted to Robotics and Autonomous System

Skin Lesion Area Segmentation Using Attention Squeeze U-Net for Embedded Devices

Melanoma is the deadliest form of skin cancer. Early diagnosis of malignant lesions is crucial for reducing mortality. The use of deep learning techniques on dermoscopic images can help in keeping track of the change over time in the appearance of the lesion, which is an important factor for detecting malignant lesions. In this paper, we present a deep learning architecture called Attention Squeeze U-Net for skin lesion area segmentation specifically designed for embedded devices. The main goal is to increase the patient empowerment through the adoption of deep learning algorithms that can run locally on smartphones or low cost embedded devices. This can be the basis to (1) create a history of the lesion, (2) reduce patient visits to the hospital, and (3) protect the privacy of the users. Quantitative results on publicly available data demonstrate that it is possible to achieve good segmentation results even with a compact model

Vision-enhanced Peg-in-Hole for automotive body parts using semantic image segmentation and object detection

Artificial Intelligence (AI) is an enabling technology in the context of Industry 4.0. In particular, the automotive sector is among those who can benefit most of the use of AI in conjunction with advanced vision techniques. The scope of this work is to integrate deep learning algorithms in an industrial scenario involving a robotic Peg-in-Hole task. More in detail, we focus on a scenario where a human operator manually positions a carbon fiber automotive part in the workspace of a 7 Degrees of Freedom (DOF) manipulator. To cope with the uncertainty on the relative position between the robot and the workpiece, we adopt a three stage strategy. The first stage concerns the Three-Dimensional (3D) reconstruction of the workpiece using a registration algorithm based on the Iterative Closest Point (ICP) paradigm. Such a procedure is integrated with a semantic image segmentation neural network, which is in charge of removing the background of the scene to improve the registration. The adoption of such network allows to reduce the registration time of about 28.8%. In the second stage, the reconstructed surface is compared with a Computer Aided Design (CAD) model of the workpiece to locate the holes and their axes. In this stage, the adoption of a Convolutional Neural Network (CNN) allows to improve the holes’ position estimation of about 57.3%. The third stage concerns the insertion of the peg by implementing a search phase to handle the remaining estimation errors. Also in this case, the use of the CNN reduces the search phase duration of about 71.3%. Quantitative experiments, including a comparison with a previous approach without both the segmentation network and the CNN, have been conducted in a realistic scenario. The results show the effectiveness of the proposed approach and how the integration of AI techniques improves the success rate from 84.5% to 99.0%

A NOVEL SEGMENTATION METHOD FOR CROWDED SCENES

Video surveillance is one of the most studied application in Computer Vision. We propose a novel method to identify and track people in a complex environment with stereo cameras. It uses two stereo cameras to deal with occlusions, two different background models that handle shadows and illumination changes and a new segmentation algorithm that is effective in crowded environments. The algorithm is able to work in real time and results demonstrating the effectiveness of the approach are shown

Crop and Weed Classification Using Pixel-wise Segmentation on Ground and Aerial Images

none5openMulham Fawakherji, Ali Youssef, Domenico D. Bloisi, Alberto Pretto, Daniele NardiFawakherji, Mulham; Youssef, Ali; Bloisi, Domenico D.; Pretto, Alberto; Nardi, Daniel

Skin lesion area segmentation using attention squeeze U-Net for embedded devices

Melanoma is the deadliest form of skin cancer. Early diagnosis of malignant lesions is crucial for reducing mortality. The use of deep learning techniques on dermoscopic images can help in keeping track of the change over time in the appearance of the lesion, which is an important factor for detecting malignant lesions. In this paper, we present a deep learning architecture called Attention Squeeze U-Net for skin lesion area segmentation specifically designed for embedded devices. The main goal is to increase the patient empowerment through the adoption of deep learning algorithms that can run locally on smartphones or low cost embedded devices. This can be the basis to (1) create a history of the lesion, (2) reduce patient visits to the hospital, and (3) protect the privacy of the users. Quantitative results on publicly available data demonstrate that it is possible to achieve good segmentation results even with a compact model