Adaptive Multi-sensor Perception for Driving Automation in Outdoor Contexts

In this research, adaptive perception for driving automation is discussed so as to enable a vehicle to automatically detect driveable areas and obstacles in the scene. It is especially designed for outdoor contexts where conventional perception systems that rely on a priori knowledge of the terrain's geometric properties, appearance properties, or both, is prone to fail, due to the variability in the terrain properties and environmental conditions. In contrast, the proposed framework uses a self-learning approach to build a model of the ground class that is continuously adjusted online to reflect the latest ground appearance. The system also features high flexibility, as it can work using a single sensor modality or a multi-sensor combination. In the context of this research, different embodiments have been demonstrated using range data coming from either a radar or a stereo camera, and adopting self-supervised strategies where monocular vision is automatically trained by radar or stereo vision. A comprehensive set of experimental results, obtained with different ground vehicles operating in the field, are presented to validate and assess the performance of the system

Deep neural networks for grape bunch segmentation in natural images from a consumer-grade camera

AbstractPrecision agriculture relies on the availability of accurate knowledge of crop phenotypic traits at the sub-field level. While visual inspection by human experts has been traditionally adopted for phenotyping estimations, sensors mounted on field vehicles are becoming valuable tools to increase accuracy on a narrower scale and reduce execution time and labor costs, as well. In this respect, automated processing of sensor data for accurate and reliable fruit detection and characterization is a major research challenge, especially when data consist of low-quality natural images. This paper investigates the use of deep learning frameworks for automated segmentation of grape bunches in color images from a consumer-grade RGB-D camera, placed on-board an agricultural vehicle. A comparative study, based on the estimation of two image segmentation metrics, i.e. the segmentation accuracy and the well-known Intersection over Union (IoU), is presented to estimate the performance of four pre-trained network architectures, namely the AlexNet, the GoogLeNet, the VGG16, and the VGG19. Furthermore, a novel strategy aimed at improving the segmentation of bunch pixels is proposed. It is based on an optimal threshold selection of the bunch probability maps, as an alternative to the conventional minimization of cross-entropy loss of mutually exclusive classes. Results obtained in field tests show that the proposed strategy improves the mean segmentation accuracy of the four deep neural networks in a range between 2.10 and 8.04%. Besides, the comparative study of the four networks demonstrates that the best performance is achieved by the VGG19, which reaches a mean segmentation accuracy on the bunch class of 80.58%, with IoU values for the bunch class of 45.64%

Scientific and Ethical Aspects of Identified Skeletal Series: The Case of the Documented Human Osteological Collections of the University of Bologna (Northern Italy)

Osteological collections are an essential source of information on human biological and cultural variability, providing insights about developmental, evolutionary, and biocultural processes. Among osteological series, documented human osteological collections (DHOC) are especially useful due to the opportunity to control biological parameters such as age-at-death and sex, which are typically unknown in archaeological or forensic cases. Raising ethical concerns about the collection, management, and study of human remains poses anthropologists with renewed responsibilities. These issues become especially pressing when dealing with DHOC. In this contribution, we discuss the scientific value and ethical issues characterizing DHOC using as case study the documented human osteological collections of the University of Bologna. This series includes more than 1000 individuals from Northern Italian and Sardinian cemeteries and is among the largest in Europe. It represents the basis for ongoing research on a large range of methodological studies, especially focused on the reconstruction of biological profile. After outlining the scientific studies performed on this DHOC, we discuss it in the context of the specific legislation featuring the Italian territory. Finally, we highlight some directions where work can be carried out to better balance scientific research, preservation needs, and ethical concerns, stressing the advantages of modern imaging techniques

Towards Intelligent Retail: Automated on-Shelf Availability Estimation Using a Depth Camera

Efficient management of on-shelf availability and inventory is a key issue to achieve customer satisfaction and reduce the risk of profit loss for both retailers and manufacturers. Conventional store audits based on physical inspection of shelves are labor-intensive and do not provide reliable assessment. This paper describes a novel framework for automated shelf monitoring, using a consumer-grade depth sensor. The aim is to develop a low-cost embedded system for early detection of out-of-stock situations with particular regard to perishable goods stored in countertop shelves, refrigerated counters, baskets or crates. The proposed solution exploits 3D point cloud reconstruction and modelling techniques, including surface fitting and occupancy grids, to estimate product availability, based on the comparison between a reference model of the shelf and its current status. No a priori knowledge about the product type is required, while the shelf reference model is automatically learnt based on an initial training stage. The output of the system can be used to generate alerts for store managers, as well as to continuously update product availability estimates for automated stock ordering and replenishment and for e-commerce apps. Experimental tests performed in a real retail environment show that the proposed system is able to estimate the on-shelf availability percentage of different fresh products with a maximum average discrepancy with respect to the actual one of about 5.0%

HCV and diabetes: Towards a 'sustained' glycaemic improvement after treatment with DAAs?

We read with interest the paper by Pavone and colleagues [1] describing the rapid reduction of fasting glucose (FG) levels in diabetic hepatitis C virus (HCV)-positive patients receiving directacting antiviral agents (DAAs). We aimed to assess if a similar decreasing trend of FG levels occurred in our study population and if it was maintained after the end of treatment (EOT). Therefore, we retrospectively evaluated 449 patients treated with DAAs at our centre (64 HIV/HCV coinfected)