Modified version of CNN-based object detection for omnidirectional vision sensors

用于全向视觉传感器的基于 CNN 的目标检测的修改版本

• 批准号: 544090-2019
• 负责人: Cheriet, Farida
• 金额: $ 1.82万
• 依托单位: École Polytechnique de Montréal
• 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
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=680913
• 关键词: Modified; version; CNN; based; object

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.

Thales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（可见光和热波段），以在变化和困难的天气条件下检测威胁物体。这些传感器采用摄像头光学器件（鱼眼镜头）制造，可以捕获180度的宽视场，以提供全景图像和更轻负载的环境总覆盖范围。这些传感器的主要问题是鱼眼镜头产生的光学畸变伪影。本项目的目的是探索一种独立于相机光学模型的灵活且低成本的软件解决方案。该解决方案不限于调整图像处理算法来校正失真。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。最佳的软件解决方案应该能够放松鱼眼相机中的失真约束，并与标准相机相比保留当前基于CNN的目标检测算法的有效性。一种新的方法需要在这个项目中开发和实施，其目标是在更少的计算资源中增加基于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在加拿大和世界各地以最小的负载和功率有效地开发和部署低成本的安全系统。此外，该项目的成功将从经济和环境角度产生重大影响。减少当前系统中固有的能量功率消耗意味着减少自然资源的消耗。