Modified version of CNN-based object detection for omnidirectional vision sensors
Modified version of CNN-based object detection for omnidirectional vision sensors
批准号:
544090-2019
负责人:
Cheriet, Farida
金额:
$1.82万
依托单位国家:
加拿大
项目类别:
Engage Grants Program
财政年份:
2019
资助国家:
加拿大
项目状态:
已结题
起止时间:
2019-01-01 至 2020-12-31
中文摘要
泰雷兹公司目前正在研究开发低成本的最佳安全态势感知系统。目前的系统基于多个视觉传感器(可见光和热波波段),用于探测各种恶劣天气条件下的威胁目标。传感器采用相机光学(鱼眼镜头)制造,允许捕获扩展180度的宽视场,以更轻的负载提供全景图像和环境的全覆盖。这些传感器的主要问题是由鱼眼镜头产生的光学畸变。这个项目的目的是探索一种灵活和低成本的软件解决方案,独立于相机的光学模型。该解决方案不限于适应图像处理算法来纠正畸变。该公司正在寻找一种最佳解决方案,以帮助降低现有态势感知系统的固有成本和功耗。最优的软件解决方案应该能够放松鱼眼相机的失真约束,并保持当前基于cnn的目标检测算法与标准相机相比的有效性。在这个项目中需要开发和实现一种新的方法,目的是在更少的计算资源下提高基于cnn的算法的预测能力。该项目的成功将使泰雷兹加拿大公司能够有效地开发和部署低成本的安全系统,在加拿大和世界范围内具有最小的负载和功率。此外,该项目的成功将从经济和环境的角度产生重大影响。减少当前系统中固有的能源消耗意味着减少自然资源的消耗。
英文摘要
Thales Company is currently investigating in developing low-cost optimal situational awareness systems for security. The current system is based on multiple vision sensors (visible and thermal wavebands) to detect threating objects under variant and difficult weather conditions. The sensors are manufactured with camera optics (fisheye lenses) that allow capturing a wide field-of-view extending 180-degrees to provide panoramic images and total coverage of the environment with lighter load. The main problem in these sensors is the optical distortion artifacts produced by fisheye lenses.The aim of this project is to explore a flexible and low-cost software solution independent of the optical model of the camera. The solution is not limited to adapting image processing algorithms to correct the distortion. The company is looking for an optimal solution that helps in reducing the inherent cost and power consumptions of the existing situational awareness system. An optimal software solution should be able to relax the distortion constraints in fisheye cameras and retain the effectiveness of the current CNN-based object detection algorithm compared to the standard cameras. A novel approach needs to be developed and implemented in this project with the objective of increasing the predictive capabilities of the CNN-based algorithm in less computation resources. The success of this project will allow Thales Canada to effectively develop and deploy low-cost security systems with minimal load and power across Canada and the world. Moreover, the success of this project will be of great impact from economic and environmental perspectives. Reducing the energy power consumption inherent in the current system implies less consumption of natural resources.
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