JCN Newswire: DENSO and Toshiba have reached a basic agreement to jointly develop a Deep Neural Network-Intellectual Property (DNN-IP), which will be used in image recognition systems which have been independently developed by the two companies for ADAS and automated driving technologies.
Because of the rapid progress in DNN technology, the two companies plan to make the technology flexibly extendable to various network configurations. They will also make the technology able to be implemented on in-vehicle processors that are smaller, consume less power, and feature other optimizations.
DENSO has been developing DNN-IP for in-vehicle applications. By incorporating DNN-IP in in-vehicle cameras, DENSO will develop high-performance, ADAS and automated driving systems. Toshiba will partition this jointly developed DNN-IP technology into dedicated hardware components and implement them on its in-vehicle image recognition processors to process images using less power than image processing systems with DSPs or GPUs.
DENSO also invests in the US-based machine learning startup THINCI. “We are thrilled DENSO is our lead investor,” said THINCI CEO Dinakar Munagala. “The automotive industry is one of the earliest adopters of vision processing and deep learning technology. DENSO’s investment in THINCI’s trailblazing solution confirms our own belief that our innovation has much to offer, not only in the automobile but in the wide range of everyday products.”
JCN Newswire: DENSO announces that the image sensors provided by Sony have helped DENSO improve the performance of its in-vehicle vision sensors and can now detect pedestrians during night conditions.
Sony image sensors, which are also used in surveillance and other monitoring devices, enable cameras to take clear images of objects even at night. DENSO has improved the quality of Sony's image sensors in terms of ease of installation, heat resistance, vibration resistance, etc. to be used in vehicle-mounted vision sensors. DENSO has also used Sony's ISPs for noise reduction and optimization of camera exposure parameters to better recognize and take clearer images of pedestrians at night.