Fast and comprehensive FPGA based BLOB analysis with the Hybrid BLOB concept

In this contribution we show our approach for a feature rich and high speed BLOB analysis on FPGAs. For the Hybrid-BLOB concept we use a combination of a single-pass BLOB analysis and a double-pass labeling algorithm. We use Basler’s VisualApplets for the implementation of the concept on their microEnable 5 frame grabbers. We achieve the extraction of the gray value data of the BLOBs at factor 14 higher frame rates compared to the naive labeling of the complete image. This is achieved by limiting the maximum BLOB size to 128 × 128 px, which speeds up the double-pass labeling algorithm. Our con-cept is targeted at low latency and high throughput demanding applications where BLOBs are small, like sensor based sorting or surface inspection

Design and Implementation of a Scalable Hardware Platform for High Speed Optical Tracking

Optical tracking has been an important subject of research since several decades. The utilization of optical tracking systems can be found in a wide range of areas, including military, medicine, industry, entertainment, etc. In this thesis a complete hardware platform that targets high-speed optical tracking applications is presented. The implemented hardware system contains three main components: a high-speed camera which is equipped with a 1.3M pixel image sensor capable of operating at 500 frames per second, a CameraLink grabber which is able to interface three cameras, and an FPGA+Dual-DSP based image processing platform. The hardware system is designed using a modular approach. The flexible architecture enables to construct a scalable optical tracking system, which allows a large number of cameras to be used in the tracking environment. One of the greatest challenges in a multi-camera based optical tracking system is the huge amounts of image data that must be processed in real-time. In this thesis, the study on FPGA based high-speed image processing is performed. The FPGA implementation for a number of image processing operators is described. How to exploit different levels of parallelisms in the algorithm to achieve high processing throughput is explained in detail. This thesis also presents a new single-pass blob analysis algorithm. With an optimized FPGA implementation, the geometrical features of a large number of blobs can be calculated in real-time. At the end of this thesis, a prototype design which integrates all the implemented hardware and software modules is demonstrated to prove the usability of the proposed optical tracking system

A Model-based Design Framework for Application-specific Heterogeneous Systems

The increasing heterogeneity of computing systems enables higher performance and power efficiency. However, these improvements come at the cost of increasing the overall complexity of designing such systems. These complexities include constructing implementations for various types of processors, setting up and configuring communication protocols, and efficiently scheduling the computational work. The process for developing such systems is iterative and time consuming, with no well-defined performance goal. Current performance estimation approaches use source code implementations that require experienced developers and time to produce. We present a framework to aid in the design of heterogeneous systems and the performance tuning of applications. Our framework supports system construction: integrating custom hardware accelerators with existing cores into processors, integrating processors into cohesive systems, and mapping computations to processors to achieve overall application performance and efficient hardware usage. It also facilitates effective design space exploration using processor models (for both existing and future processors) that do not require source code implementations to estimate performance. We evaluate our framework using a variety of applications and implement them in systems ranging from low power embedded systems-on-chip (SoC) to high performance systems consisting of commercial-off-the-shelf (COTS) components. We show how the design process is improved, reducing the number of design iterations and unnecessary source code development ultimately leading to higher performing efficient systems

Forum Bildverarbeitung 2022

Bildverarbeitung verknüpft das Fachgebiet die Sensorik von Kameras – bildgebender Sensorik – mit der Verarbeitung der Sensordaten – den Bildern. Daraus resultiert der besondere Reiz dieser Disziplin. Der vorliegende Tagungsband des „Forums Bildverarbeitung“, das am 24. und 25.11.2022 in Karlsruhe als Veranstaltung des Karlsruher Instituts für Technologie und des Fraunhofer-Instituts für Optronik, Systemtechnik und Bildauswertung stattfand, enthält die Aufsätze der eingegangenen Beiträge

Slanted Stixels: A way to represent steep streets

This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a Fully Convolutional Network (FCN), which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.Comment: Journal preprint (published in IJCV 2019: https://link.springer.com/article/10.1007/s11263-019-01226-9). arXiv admin note: text overlap with arXiv:1707.0539

A FPGA-based architecture for real-time cluster finding in the LHCb silicon pixel detector

The data acquisition system of the LHCb experiment has been substantially upgraded for the LHC Run 3, with the unprecedented capability of reading out and fully reconstructing all proton–proton collisions in real time, occurring with an average rate of 30 MHz, for a total data flow of approximately 32 Tb/s. The high demand of computing power required by this task has motivated a transition to a hybrid heterogeneous computing architecture, where a farm of graphics cores, GPUs, is used in addition to general–purpose processors, CPUs, to speed up the execution of reconstruction algorithms. In a continuing effort to improve real–time processing capabilities of this new DAQ system, also with a view to further luminosity increases in the future, low–level, highly–parallelizable tasks are increasingly being addressed at the earliest stages of the data acquisition chain, using special–purpose computing accelerators. A promising solution is offered by custom–programmable FPGA devices, that are well suited to perform high–volume computations with high throughput and degree of parallelism, limited power consumption and latency. In this context, a two–dimensional FPGA–friendly cluster–finder algorithm has been developed to reconstruct hit positions in the new vertex pixel detector (VELO) of the LHCb Upgrade experiment. The associated firmware architecture, implemented in VHDL language, has been integrated within the VELO readout, without the need for extra cards, as a further enhancement of the DAQ system. This pre–processing allows the first level of the software trigger to accept a 11% higher rate of events, as the ready– made hit coordinates accelerate the track reconstruction, while leading to a drop in electrical power consumption, as the FPGA implementation requires O(50x) less power than the GPU one. The tracking performance of this novel system, being indistinguishable from a full–fledged software implementation, allows the raw pixel data to be dropped immediately at the readout level, yielding the additional benefit of a 14% reduction in data flow. The clustering architecture has been commissioned during the start of LHCb Run 3 and it currently runs in real time during physics data taking, reconstructing VELO hit coordinates on–the–fly at the LHC collision rate

Forum Bildverarbeitung 2022

Image processing combines the disciplines of cameras – image-based sensors – with the processing of the sensor data – the images. From this follows the particular attraction of this field. The conference proceedings at hand of the “Image Processing Forum”, which took place on 24.-25.11.2022 in Karlsruhe as a common event of the Karlsruhe Institute of Technology and the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, contain the articles of the contributions

Intelligent Agricultural Machinery Using Deep Learning

Artificial intelligence, deep learning, big data, self-driving cars, these are words that have become familiar to most people and have captured the imagination of the public and have brought hopes as well as fears. We have been told that artificial intelligence will be a major part of our lives, and almost all of us witness this when decisions made by algorithms show us commercial advertisements that specifically target our interests while using the web. In this paper, the conversation around artificial intelligence focuses on a particular application, agricultural machinery, but offers enough content so that the reader can have a very good idea on how to consider this technology for not only other agricultural applications such as sorting and grading produce, but also other areas in which this technology can be a part of a system that includes sensors, hardware and software that can make accurate decisions. Narrowing the application and also focusing on one specific artificial intelligence approach, that of deep learning, allow us to illustrate from start to end the steps that are usually considered and elaborate on recent developments on artificial intelligence