LiDAR Object Detection Utilizing Existing CNNs for Smart Cities

As governments and private companies alike race to achieve the vision of a smart city — where artificial intelligence (AI) technology is used to enable self-driving cars, cashier-less shopping experiences and connected home devices from thermostats to robot vacuum cleaners — advancements are being made in both software and hardware to enable increasingly real-time, accurate inference at the edge. One hardware solution adopted for this purpose is the LiDAR sensor, which utilizes infrared lasers to accurately detect and map its surroundings in 3D. On the software side, developers have turned to artificial neural networks to make predictions and recommendations with high accuracy. These neural networks have the potential, particularly run on purpose-built hardware such as GPUs and TPUs, to make inferences in near real-time, allowing the AI models to serve as a usable interface for real-world interactions with other AI-powered devices, or with human users. This paper aims to example the joint use of LiDAR sensors and AI to understand its importance in smart city environments

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Towards autonomous driving: a machine learning-based pedestrian detection system using 16-layer LiDAR

The advent of driverless and automated vehicle technologies opens up a new era of safe and comfortable transportation. However, one of the most important features that an autonomous vehicle requires, is a reliable pedestrian detection mechanism. Many solutions have been proposed in the literature to achieve this technology, ranging from image processing algorithms applied on a camera feed, to filtering LiDAR scans for points that are reflected off pedestrians. To this extent, this paper proposes a machine learning-based pedestrian detection mechanism using a 16-layer Velodyne Puck LITE LiDAR. The proposed mechanism compensates for the low resolution of the LiDAR through the use of linear interpolation between layers, effectively introducing 15 pseudo-layers to help obtain timely detection at practical distances. The pedestrian candidates are then classified u sing a Support Vector Machine ( SVM), and the algorithm is verified by accuracy testing using real LiDAR frames acquired under different road scenarios

Deep learning based 3D object detection for automotive radar and camera fusion

La percepción en el dominio de los vehículos autónomos es una disciplina clave para lograr la automatización de los Sistemas Inteligentes de Transporte. Por ello, este Trabajo Fin de Máster tiene como objetivo el desarrollo de una técnica de fusión sensorial para RADAR y cámara que permita crear una representación del entorno enriquecida para la Detección de Objetos 3D mediante algoritmos Deep Learning. Para ello, se parte de la idea de PointPainting [1] y se adapta a un sensor en auge, el RADAR 3+1D, donde nube de puntos RADAR e información semántica de la cámara son agregadas para generar una representación enriquecida del entorno.Perception in the domain of autonomous vehicles is a key discipline to achieve the au tomation of Intelligent Transport Systems. Therefore, this Master Thesis aims to develop a sensor fusion technique for RADAR and camera to create an enriched representation of the environment for 3D Object Detection using Deep Learning algorithms. To this end, the idea of PointPainting [1] is used as a starting point and is adapted to a growing sensor, the 3+1D RADAR, in which the radar point cloud is aggregated with the semantic information from the camera.Máster Universitario en Ingeniería Industrial (M141

ThermRad: A Multi-modal Dataset for Robust 3D Object Detection under Challenging Conditions

Robust 3D object detection in extreme weather and illumination conditions is a challenging task. While radars and thermal cameras are known for their resilience to these conditions, few studies have been conducted on radar-thermal fusion due to the lack of corresponding datasets. To address this gap, we first present a new multi-modal dataset called ThermRad, which includes a 3D LiDAR, a 4D radar, an RGB camera and a thermal camera. This dataset is unique because it includes data from all four sensors in extreme weather conditions, providing a valuable resource for future research in this area. To validate the robustness of 4D radars and thermal cameras for 3D object detection in challenging weather conditions, we propose a new multi-modal fusion method called RTDF-RCNN, which leverages the complementary strengths of 4D radars and thermal cameras to boost object detection performance. To further prove the effectiveness of our proposed framework, we re-implement state-of-the-art (SOTA) 3D detectors on our dataset as benchmarks for evaluation. Our method achieves significant enhancements in detecting cars, pedestrians, and cyclists, with improvements of over 7.98%, 24.27%, and 27.15%, respectively, while achieving comparable results to LiDAR-based approaches. Our contributions in both the ThermRad dataset and the new multi-modal fusion method provide a new approach to robust 3D object detection in adverse weather and illumination conditions. The ThermRad dataset will be released.Comment: 12 pages, 5 figures, Proceedings of the IEEE/CVF International Conference on Computer Visio